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super big huge update

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This commit is contained in:
dr. M.S. (Matthijs) Berends 2022-09-16 23:15:23 +02:00
parent 63fe160322
commit ae9059ab99
111 changed files with 2269 additions and 89912 deletions
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6
.github/prehooks/pre-commit vendored
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@ -7,11 +7,11 @@ if command -v Rscript > /dev/null; then
if [ "$(Rscript -e 'cat(all(c('"'pkgload'"', '"'devtools'"', '"'dplyr'"', '"'styler'"') %in% rownames(installed.packages())))')" = "TRUE" ]; then
Rscript -e "source('data-raw/_pre_commit_hook.R')"
currentpkg=`Rscript -e "cat(pkgload::pkg_name())"`
echo "-> Adding all files in folders 'data-raw', 'inst', 'man', and 'R' to this git commit"
echo "-> Adding all files in folders 'data-raw' and 'man' to this git commit"
git add data-raw/*
git add inst/*
git add man/*
git add R/*
# git add inst/*
# git add R/*
else
echo "- R package 'pkgload', 'devtools', 'dplyr', or 'styler' not installed!"
currentpkg="your"

2
.github/workflows/check.yaml vendored
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@ -156,4 +156,4 @@ jobs:
uses: actions/upload-artifact@v2
with:
name: artifacts-${{ matrix.config.os }}-r${{ matrix.config.r }}
path: /home/runner/work/AMR.Rcheck
path: /home/runner/work/AMR/AMR.Rcheck

13
.github/workflows/lintr.yaml vendored
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@ -52,5 +52,16 @@ jobs:
extra-packages: any::lintr
- name: Lint
run: lintr::lint_package(linters = lintr::with_defaults(line_length_linter = NULL, trailing_whitespace_linter = NULL, object_name_linter = NULL, cyclocomp_linter = NULL, object_length_linter = lintr::object_length_linter(length = 50L)), exclusions = list("R/aa_helper_pm_functions.R"))
run: |
# old: lintr::lint_package(linters = lintr::with_defaults(line_length_linter = NULL, trailing_whitespace_linter = NULL, object_name_linter = NULL, cyclocomp_linter = NULL, object_length_linter = lintr::object_length_linter(length = 50L)), exclusions = list("R/aa_helper_pm_functions.R"))
# now get ALL linters, not just default ones
linters <- ls(envir = asNamespace("lintr"), pattern = "_linter$")
# lose deprecated
linters <- linters[!grepl("^(closed_curly|open_curly|paren_brace|semicolon_terminator)_linter$", linters)]
# and the ones we find unnnecessary
linters <- linters[!grepl("^(extraction_operator|implicit_integer|line_length|object_name|nonportable_path)_linter$", linters)]
# put the functions in a list
linters <- lapply(linters, function(l) eval(parse(text = paste0("lintr::", l, "()")), envir = asNamespace("lintr")))
# run them all!
lintr::lint_package(linters = linters, exclusions = list("R/aa_helper_pm_functions.R"))
shell: Rscript {0}

2
AMR.Rproj
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@ -23,3 +23,5 @@ PackageCheckArgs: --no-build-vignettes --as-cran
PackageRoxygenize: rd,collate,namespace
UseNativePipeOperator: No
SpellingDictionary: en_GB

7
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 1.8.1.9049
Date: 2022-09-01
Version: 1.8.1.9050
Date: 2022-09-16
Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR)
data analysis and to work with microbial and antimicrobial properties by
@ -29,8 +29,9 @@ Authors@R: c(
Depends: R (>= 3.0.0)
Enhances:
cleaner,
skimr,
ggplot2,
janitor,
skimr,
tibble,
tidyselect
Suggests:

3
NAMESPACE
View File

@ -114,7 +114,6 @@ S3method(plot,resistance_predict)
S3method(plot,rsi)
S3method(print,ab)
S3method(print,bug_drug_combinations)
S3method(print,catalogue_of_life_version)
S3method(print,custom_eucast_rules)
S3method(print,custom_mdro_guideline)
S3method(print,disk)
@ -207,7 +206,6 @@ export(betalactams)
export(brmo)
export(bug_drug_combinations)
export(carbapenems)
export(catalogue_of_life_version)
export(cephalosporins)
export(cephalosporins_1st)
export(cephalosporins_2nd)
@ -289,6 +287,7 @@ export(mo_property)
export(mo_rank)
export(mo_ref)
export(mo_renamed)
export(mo_reset_session)
export(mo_shortname)
export(mo_snomed)
export(mo_species)

30
NEWS.md
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@ -1,12 +1,27 @@
# AMR 1.8.1.9049
# AMR 1.8.1.9050
This version will eventually become v2.0! We're happy to reach a new major milestone soon!
### Breaking
* Removed all species of the taxonomic kingdom Chromista from the package. This was done for multiple reasons:
* CRAN allows packages to be around 5 MB maximum, some packages are exempted but this package is not one of them
* Chromista are not relevant when it comes to antimicrobial resistance, thus lacking the primary scope of this package
* Chromista are almost never clinically relevant, thus lacking the secondary scope of this package
* The `microorganisms` no longer relies on the Catalogue of Life, but now primarily on the List of Prokaryotic names with Standing in Nomenclature (LPSN) and is supplemented with the Global Biodiversity Information Facility (GBIF). The structure of this data set has changed to include separate LPSN and GBIF identifiers. Almost all previous MO codes were retained. It contains over 1,000 taxonomic names from 2022 already.
* The `microorganisms.old` data set was removed, and all previously accepted names are now included in the `microorganisms` data set. A new column `status` contains `"accepted"` for currently accepted names and `"synonym"` for taxonomic synonyms; currently invalid names. All previously accepted names now have a microorganisms ID and - if available - an LPSN, GBIF and SNOMED CT identifier.
### New
* EUCAST 2022 and CLSI 2022 guidelines have been added for `as.rsi()`. EUCAST 2022 is now the new default guideline for all MIC and disks diffusion interpretations.
* All new algorithm for `as.mo()` (and thus internally all `mo_*()` functions) while still following our original set-up as described in our paper.
* A new argument `keep_synonyms` allows to *not* correct for updated taxonomy
* It has increased tremendously in speed and returns generally more consequent results
* Sequential coercion is now extremely fast as results are stored to the package environment, although coercion of unknown values must be run once per session. Previous results can be reset/removed with the new `mo_reset_session()` function.
* Function `mean_amr_distance()` to calculate the mean AMR distance. The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand.
* Function `rsi_interpretation_history()` to view the history of previous runs of `as.rsi()`. This returns a 'logbook' with the selected guideline, reference table and specific interpretation of each row in a data set on which `as.rsi()` was run.
* Support for `data.frame`-enhancing R packages, more specifically: `data.table`, `tibble`, and `tsibble`. AMR package functions that have a data set as output (such as `rsi_df()` and `bug_drug_combinations()`), will now return the same data type as the input. Furthermore, all our data sets are now in `tibble` format.
* Our data sets are now also continually exported to Apache Feather and Apache Parquet formats. You can find more info [in this article on our website](https://msberends.github.io/AMR/articles/datasets.html).
* Support for `data.frame`-enhancing R packages, more specifically: `data.table::data.table`, `janitor::tabyl`, `tibble::tibble`, and `tsibble::tsibble`. AMR package functions that have a data set as output (such as `rsi_df()` and `bug_drug_combinations()`), will now return the same data type as the input.
* All data sets in this package are now exported as `tibble`, instead of base R `data.frame`s. Older R versions are still supported.
* Support for the following languages: Chinese, Greek, Japanese, Polish, Turkish and Ukrainian. We are very grateful for the valuable input by our colleagues from other countries. The `AMR` package is now available in 16 languages.
* Our data sets are now also continually exported to Apache Feather and Apache Parquet formats. You can find more info [in this article on our website](https://msberends.github.io/AMR/articles/datasets.html).
### Changed
* Fix for using `as.rsi()` on certain EUCAST breakpoints for MIC values
@ -18,19 +33,20 @@
* Using any `random_*()` function (such as `random_mic()`) is now possible by directly calling the package without loading it first: `AMR::random_mic(10)`
* Added *Toxoplasma gondii* (`P_TXPL_GOND`) to the `microorganisms` data set, together with its genus, family, and order
* Changed value in column `prevalence` of the `microorganisms` data set from 3 to 2 for these genera: *Acholeplasma*, *Alistipes*, *Alloprevotella*, *Bergeyella*, *Borrelia*, *Brachyspira*, *Butyricimonas*, *Cetobacterium*, *Chlamydia*, *Chlamydophila*, *Deinococcus*, *Dysgonomonas*, *Elizabethkingia*, *Empedobacter*, *Haloarcula*, *Halobacterium*, *Halococcus*, *Myroides*, *Odoribacter*, *Ornithobacterium*, *Parabacteroides*, *Pedobacter*, *Phocaeicola*, *Porphyromonas*, *Riemerella*, *Sphingobacterium*, *Streptobacillus*, *Tenacibaculum*, *Terrimonas*, *Victivallis*, *Wautersiella*, *Weeksella*
* Fix for using the form `df[carbapenems() == "R", ]` using the latest `vctrs` package
* Fix for using the form `df[carbapenems() == "R", ]` when using the latest `vctrs` package
* Fix for using `info = FALSE` in `mdro()`
* All data sets in this package are now exported as `tibble`, instead of base R `data.frame`s. Older R versions are still supported.
* Automatic language determination will give a note once a session
* For all interpretation guidelines using `as.rsi()` on amoxicillin, the rules for ampicillin will be used if amoxicillin rules are not available
* Fix for using `ab_atc()` on non-existing ATC codes
* Black and white message texts are now reversed in colour if using an RStudio dark theme
* `mo_snomed()` now returns class `character`, not `numeric` anymore (to make long SNOMED codes readable)
### Other
* New website to make use of the new Bootstrap 5 and pkgdown v2.0. The website now contains results for all examples and will be automatically regenerated with every change to our repository, using GitHub Actions
* Added Peter Dutey-Magni and Anton Mymrikov as contributors, to thank them for their valuable input
* Set up Git Large File Storage (Git LFS) for the large SAS and SPSS file formats
* Added Peter Dutey-Magni, Dmytro Mykhailenko and Anton Mymrikov as contributors, to thank them for their valuable input
* All R and Rmd files in this project are now styled using the `styler` package
* Set scalar conditional expressions (`&&` and `||`) where possible to comply with the upcoming R 4.3
* An enormous lot of code cleaning, fixing some small bugs on the way
# `AMR` 1.8.1

32
R/aa_globals.R
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@ -60,21 +60,22 @@ EUCAST_VERSION_EXPERT_RULES <- list(
)
)
SNOMED_VERSION <- list(
title = "Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS)",
current_source = "US Edition of SNOMED CT from 1 September 2020",
current_version = 12,
current_oid = "2.16.840.1.114222.4.11.1009",
value_set_name = "Microorganism",
url = "https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009"
)
CATALOGUE_OF_LIFE <- list(
year = 2019,
version = "Catalogue of Life: {year} Annual Checklist",
url_CoL = "http://www.catalogueoflife.org",
url_LPSN = "https://lpsn.dsmz.de",
yearmonth_LPSN = "5 October 2021"
TAXONOMY_VERSION <- list(
GBIF = list(
accessed_date = as.Date("2022-09-12"),
citation = "GBIF Secretariat (November 26, 2021). GBIF Backbone Taxonomy. Checklist dataset \\doi{10.15468/39omei}.",
url = "https://www.gbif.org"
),
LPSN = list(
accessed_date = as.Date("2022-09-12"),
citation = "Parte, A.C. *et al.* (2020). **List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ.** International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; \\doi{10.1099/ijsem.0.004332}.",
url = "https://lpsn.dsmz.de"
),
SNOMED = list(
accessed_date = as.Date("2021-07-01"),
citation = "Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12).",
url = "https://phinvads.cdc.gov"
)
)
globalVariables(c(
@ -117,7 +118,6 @@ globalVariables(c(
"microorganism",
"microorganisms",
"microorganisms.codes",
"microorganisms.old",
"mo",
"name",
"new",

338
R/aa_helper_functions.R
View File

@ -81,7 +81,7 @@ where <- function(fn) {
quick_case_when <- function(...) {
fs <- list(...)
lapply(fs, function(x) {
if (class(x) != "formula") {
if (!inherits(x, "formula")) {
stop("`case_when()` requires formula inputs.")
}
})
@ -208,63 +208,6 @@ addin_insert_like <- function() {
}
}
check_dataset_integrity <- function() {
# check if user overwrote our data sets in their global environment
data_in_pkg <- data(package = "AMR", envir = asNamespace("AMR"))$results[, "Item", drop = TRUE]
data_in_globalenv <- ls(envir = globalenv())
overwritten <- data_in_pkg[data_in_pkg %in% data_in_globalenv]
# exception for example_isolates
overwritten <- overwritten[overwritten %unlike% "example_isolates"]
if (length(overwritten) > 0) {
if (length(overwritten) > 1) {
plural <- c("s are", "", "s")
} else {
plural <- c(" is", "s", "")
}
if (message_not_thrown_before("check_dataset_integrity", overwritten)) {
warning_(
"The following data set", plural[1],
" overwritten by your global environment and prevent", plural[2],
" the AMR package from working correctly: ",
vector_and(overwritten, quotes = "'"),
".\nPlease rename your object", plural[3], "."
)
}
}
# check if other packages did not overwrite our data sets
valid_microorganisms <- TRUE
valid_antibiotics <- TRUE
tryCatch(
{
valid_microorganisms <- all(c(
"mo", "fullname", "kingdom", "phylum",
"class", "order", "family", "genus",
"species", "subspecies", "rank",
"species_id", "source", "ref", "prevalence"
) %in% colnames(microorganisms),
na.rm = TRUE
)
valid_antibiotics <- all(c(
"ab", "atc", "cid", "name", "group",
"atc_group1", "atc_group2", "abbreviations",
"synonyms", "oral_ddd", "oral_units",
"iv_ddd", "iv_units", "loinc"
) %in% colnames(antibiotics),
na.rm = TRUE
)
},
error = function(e) {
# package not yet loaded
require("AMR")
}
)
stop_if(
!valid_microorganisms | !valid_antibiotics,
"the data set `microorganisms` or `antibiotics` was overwritten in your environment because another package with the same object name(s) was loaded _after_ the AMR package, preventing the AMR package from working correctly. Please load the AMR package last."
)
invisible(TRUE)
}
search_type_in_df <- function(x, type, info = TRUE) {
meet_criteria(x, allow_class = "data.frame")
meet_criteria(type, allow_class = "character", has_length = 1)
@ -281,8 +224,8 @@ search_type_in_df <- function(x, type, info = TRUE) {
if (any(vapply(FUN.VALUE = logical(1), x, is.mo))) {
# take first <mo> column
found <- colnames(x)[vapply(FUN.VALUE = logical(1), x, is.mo)]
} else if ("mo" %in% colnames_formatted &
suppressWarnings(all(x$mo %in% c(NA, microorganisms$mo)))) {
} else if ("mo" %in% colnames_formatted &&
suppressWarnings(all(x$mo %in% c(NA, AMR::microorganisms$mo)))) {
found <- "mo"
} else if (any(colnames_formatted %like_case% "^(mo|microorganism|organism|bacteria|ba[ck]terie)s?$")) {
found <- sort(colnames(x)[colnames_formatted %like_case% "^(mo|microorganism|organism|bacteria|ba[ck]terie)s?$"])
@ -303,7 +246,7 @@ search_type_in_df <- function(x, type, info = TRUE) {
if (any(colnames_formatted %like_case% "^(specimen date|specimen_date|spec_date)")) {
# WHONET support
found <- sort(colnames(x)[colnames_formatted %like_case% "^(specimen date|specimen_date|spec_date)"])
if (!any(class(pm_pull(x, found)) %in% c("Date", "POSIXct"))) {
if (!inherits(pm_pull(x, found), c("Date", "POSIXct"))) {
stop(font_red(paste0(
"Found column '", font_bold(found), "' to be used as input for `col_", type,
"`, but this column contains no valid dates. Transform its values to valid dates first."
@ -357,7 +300,7 @@ search_type_in_df <- function(x, type, info = TRUE) {
found <- found[1]
if (!is.null(found) & info == TRUE) {
if (!is.null(found) && info == TRUE) {
if (message_not_thrown_before("search_in_type", type)) {
msg <- paste0("Using column '", font_bold(found), "' as input for `col_", type, "`.")
if (type %in% c("keyantibiotics", "keyantimicrobials", "specimen")) {
@ -372,7 +315,7 @@ search_type_in_df <- function(x, type, info = TRUE) {
is_valid_regex <- function(x) {
regex_at_all <- tryCatch(vapply(
FUN.VALUE = logical(1),
X = strsplit(x, ""),
X = strsplit(x, "", fixed = TRUE),
FUN = function(y) {
any(y %in% c(
"$", "(", ")", "*", "+", "-",
@ -390,9 +333,7 @@ is_valid_regex <- function(x) {
FUN.VALUE = logical(1),
X = x,
FUN = function(y) {
!"try-error" %in% class(try(grepl(y, "", perl = TRUE),
silent = TRUE
))
!inherits(try(grepl(y, "", perl = TRUE), silent = TRUE), "try-error")
},
USE.NAMES = FALSE
)
@ -471,7 +412,7 @@ word_wrap <- function(...,
# run word_wraps() over every line here, bind them and return again
return(paste0(vapply(
FUN.VALUE = character(1),
trimws(unlist(strsplit(msg, "\n")), which = "right"),
trimws(unlist(strsplit(msg, "\n", fixed = TRUE)), which = "right"),
word_wrap,
add_fn = add_fn,
as_note = FALSE,
@ -497,12 +438,12 @@ word_wrap <- function(...,
msg_stripped_wrapped <- paste0(unlist(strsplit(msg_stripped_wrapped, "(\n|\\*\\|\\*)")),
collapse = "\n"
)
msg_stripped_spaces <- which(unlist(strsplit(msg_stripped, "")) == " ")
msg_stripped_wrapped_spaces <- which(unlist(strsplit(msg_stripped_wrapped, "")) != "\n")
msg_stripped_spaces <- which(unlist(strsplit(msg_stripped, "", fixed = TRUE)) == " ")
msg_stripped_wrapped_spaces <- which(unlist(strsplit(msg_stripped_wrapped, "", fixed = TRUE)) != "\n")
# so these are the indices of spaces that need to be replaced
replace_spaces <- which(!msg_stripped_spaces %in% msg_stripped_wrapped_spaces)
# put it together
msg <- unlist(strsplit(msg, " "))
msg <- unlist(strsplit(msg, " ", fixed = TRUE))
msg[replace_spaces] <- paste0(msg[replace_spaces], "\n")
# add space around operators again
msg <- gsub(paste0(ops, ops), "\\1 \\2", msg, perl = TRUE)
@ -534,6 +475,8 @@ word_wrap <- function(...,
# clean introduced whitespace between fullstops
msg <- gsub("[.] +[.]", "..", msg)
# remove extra space that was introduced (case: "Smith et al., 2022")
msg <- gsub(". ,", ".,", msg, fixed = TRUE)
msg
}
@ -608,17 +551,14 @@ stop_ifnot <- function(expr, ..., call = TRUE) {
}
"%or%" <- function(x, y) {
if (is.null(x) | is.null(y)) {
if (is.null(x) || is.null(y)) {
if (is.null(x)) {
return(y)
} else {
return(x)
}
}
ifelse(!is.na(x),
x,
ifelse(!is.na(y), y, NA)
)
ifelse(is.na(x), y, x)
}
return_after_integrity_check <- function(value, type, check_vector) {
@ -654,9 +594,29 @@ dataset_UTF8_to_ASCII <- function(df) {
import_fn("as_tibble", "tibble")(df)
}
documentation_date <- function(d) {
paste0(trimws(format(d, "%e")), " ", month.name[as.integer(format(d, "%m"))], ", ", format(d, "%Y"))
}
format_included_data_number <- function(data) {
if (is.data.frame(data)) {
n <- nrow(data)
} else {
n <- length(unique(data))
}
if (n > 10000) {
rounder <- -3 # round on thousands
} else if (n > 1000) {
rounder <- -2 # round on hundreds
} else {
rounder <- -1 # round on tens
}
paste0("~", format(round(n, rounder), decimal.mark = ".", big.mark = ","))
}
# for eucast_rules() and mdro(), creates markdown output with URLs and names
create_eucast_ab_documentation <- function() {
x <- trimws(unique(toupper(unlist(strsplit(EUCAST_RULES_DF$then_change_these_antibiotics, ",")))))
x <- trimws(unique(toupper(unlist(strsplit(EUCAST_RULES_DF$then_change_these_antibiotics, ",", fixed = TRUE)))))
ab <- character()
for (val in x) {
if (paste0("AB_", val) %in% ls(envir = asNamespace("AMR"))) {
@ -731,7 +691,8 @@ format_class <- function(class, plural = FALSE) {
class[class %in% c("number", "whole number")] <- "(whole) number"
}
class[class == "character"] <- "text string"
class[class %in% c("Date", "POSIXt")] <- "date"
class[class == "Date"] <- "date"
class[class %in% c("POSIXt", "POSIXct", "POSIXlt")] <- "date/time"
class[class != class.bak] <- paste0(
ifelse(plural, "", "a "),
class[class != class.bak],
@ -1010,10 +971,12 @@ unique_call_id <- function(entire_session = FALSE, match_fn = NULL) {
))
}
}
} else if (identical(Sys.getenv("R_RUN_TINYTEST"), "true")) {
message("NOTE: env R_RUN_TINYTEST is set to 'true', unique_call_id() not working well")
}
c(
envir = paste0(sample(c(c(0:9), letters[1:6]), size = 32, replace = TRUE), collapse = ""),
call = paste0(sample(c(c(0:9), letters[1:6]), size = 32, replace = TRUE), collapse = "")
envir = paste0(sample(c(0:9, letters[1:6]), size = 32, replace = TRUE), collapse = ""),
call = paste0(sample(c(0:9, letters[1:6]), size = 32, replace = TRUE), collapse = "")
)
}
@ -1100,7 +1063,10 @@ has_colour <- function() {
# set colours if console has_colour()
try_colour <- function(..., before, after, collapse = " ") {
txt <- paste0(unlist(list(...)), collapse = collapse)
if (length(c(...)) == 0) {
return(character(0))
}
txt <- paste0(c(...), collapse = collapse)
if (isTRUE(has_colour())) {
if (is.null(collapse)) {
paste0(before, txt, after, collapse = NULL)
@ -1166,26 +1132,26 @@ font_grey_bg <- function(..., collapse = " ") {
try_colour(..., before = "\033[48;5;255m", after = "\033[49m", collapse = collapse)
}
}
font_green_bg <- function(..., collapse = " ") {
try_colour(..., before = "\033[42m", after = "\033[49m", collapse = collapse)
}
font_rsi_R_bg <- function(..., collapse = " ") {
# ED553B
try_colour(..., before = "\033[48;5;203m", after = "\033[49m", collapse = collapse)
}
font_rsi_S_bg <- function(..., collapse = " ") {
# 3CAEA3
try_colour(..., before = "\033[48;5;79m", after = "\033[49m", collapse = collapse)
}
font_rsi_I_bg <- function(..., collapse = " ") {
# F6D55C
try_colour(..., before = "\033[48;5;222m", after = "\033[49m", collapse = collapse)
}
font_red_bg <- function(..., collapse = " ") {
try_colour(..., before = "\033[41m", after = "\033[49m", collapse = collapse)
# this is #ed553b (picked to be colourblind-safe with other RSI colours)
try_colour(font_black(..., collapse = collapse), before = "\033[48;5;203m", after = "\033[49m", collapse = collapse)
}
font_orange_bg <- function(..., collapse = " ") {
# this is #f6d55c (picked to be colourblind-safe with other RSI colours)
try_colour(font_black(..., collapse = collapse), before = "\033[48;5;222m", after = "\033[49m", collapse = collapse)
}
font_yellow_bg <- function(..., collapse = " ") {
try_colour(..., before = "\033[43m", after = "\033[49m", collapse = collapse)
try_colour(font_black(..., collapse = collapse), before = "\033[48;5;228m", after = "\033[49m", collapse = collapse)
}
font_green_bg <- function(..., collapse = " ") {
# this is #3caea3 (picked to be colourblind-safe with other RSI colours)
try_colour(font_black(..., collapse = collapse), before = "\033[48;5;79m", after = "\033[49m", collapse = collapse)
}
font_purple_bg <- function(..., collapse = " ") {
try_colour(font_black(..., collapse = collapse), before = "\033[48;5;89m", after = "\033[49m", collapse = collapse)
}
font_rose_bg <- function(..., collapse = " ") {
try_colour(font_black(..., collapse = collapse), before = "\033[48;5;217m", after = "\033[49m", collapse = collapse)
}
font_na <- function(..., collapse = " ") {
font_red(..., collapse = collapse)
@ -1281,61 +1247,21 @@ as_original_data_class <- function(df, old_class = NULL) {
fn <- import_fn("as_tsibble", "tsibble")
} else if ("data.table" %in% old_class && pkg_is_available("data.table", also_load = FALSE)) {
fn <- import_fn("as.data.table", "data.table")
} else if ("tabyl" %in% old_class && pkg_is_available("janitor", also_load = FALSE)) {
fn <- import_fn("as_tabyl", "janitor")
} else {
fn <- base::as.data.frame
}
fn(df)
}
# copied from vctrs::s3_register by their permission:
# https://github.com/r-lib/vctrs/blob/05968ce8e669f73213e3e894b5f4424af4f46316/R/register-s3.R
s3_register <- function(generic, class, method = NULL) {
stopifnot(is.character(generic), length(generic) == 1)
stopifnot(is.character(class), length(class) == 1)
pieces <- strsplit(generic, "::")[[1]]
stopifnot(length(pieces) == 2)
package <- pieces[[1]]
generic <- pieces[[2]]
caller <- parent.frame()
get_method_env <- function() {
top <- topenv(caller)
if (isNamespace(top)) {
asNamespace(environmentName(top))
} else {
caller
}
}
get_method <- function(method, env) {
if (is.null(method)) {
get(paste0(generic, ".", class), envir = get_method_env())
} else {
method
}
}
method_fn <- get_method(method)
stopifnot(is.function(method_fn))
setHook(packageEvent(package, "onLoad"), function(...) {
ns <- asNamespace(package)
method_fn <- get_method(method)
registerS3method(generic, class, method_fn, envir = ns)
})
if (!isNamespaceLoaded(package)) {
return(invisible())
}
envir <- asNamespace(package)
if (exists(generic, envir)) {
registerS3method(generic, class, method_fn, envir = envir)
}
invisible()
}
# works exactly like round(), but rounds `round2(44.55, 1)` to 44.6 instead of 44.5
# and adds decimal zeroes until `digits` is reached when force_zero = TRUE
round2 <- function(x, digits = 1, force_zero = TRUE) {
x <- as.double(x)
# https://stackoverflow.com/a/12688836/4575331
val <- (trunc((abs(x) * 10^digits) + 0.5) / 10^digits) * sign(x)
if (digits > 0 & force_zero == TRUE) {
if (digits > 0 && force_zero == TRUE) {
values_trans <- val[val != as.integer(val) & !is.na(val)]
val[val != as.integer(val) & !is.na(val)] <- paste0(
values_trans,
@ -1433,10 +1359,90 @@ time_track <- function(name = NULL) {
paste("(until now:", trimws(round(as.double(Sys.time()) * 1000) - pkg_env$time_start), "ms)")
}
# prevent dependency on package 'backports' ----
# these functions were not available in previous versions of R (last checked: R 4.1.0)
# nolint start
# Register S3 methods ----
# copied from vctrs::s3_register by their permission:
# https://github.com/r-lib/vctrs/blob/05968ce8e669f73213e3e894b5f4424af4f46316/R/register-s3.R
s3_register <- function(generic, class, method = NULL) {
stopifnot(is.character(generic), length(generic) == 1)
stopifnot(is.character(class), length(class) == 1)
pieces <- strsplit(generic, "::")[[1]]
stopifnot(length(pieces) == 2)
package <- pieces[[1]]
generic <- pieces[[2]]
caller <- parent.frame()
get_method_env <- function() {
top <- topenv(caller)
if (isNamespace(top)) {
asNamespace(environmentName(top))
} else {
caller
}
}
get_method <- function(method, env) {
if (is.null(method)) {
get(paste0(generic, ".", class), envir = get_method_env())
} else {
method
}
}
method_fn <- get_method(method)
stopifnot(is.function(method_fn))
setHook(packageEvent(package, "onLoad"), function(...) {
ns <- asNamespace(package)
method_fn <- get_method(method)
registerS3method(generic, class, method_fn, envir = ns)
})
if (!isNamespaceLoaded(package)) {
return(invisible())
}
envir <- asNamespace(package)
if (exists(generic, envir)) {
registerS3method(generic, class, method_fn, envir = envir)
}
invisible()
}
# Support old R versions ----
# these functions were not available in previous versions of R
# see here for the full list: https://github.com/r-lib/backports
strrep <- function(x, times) {
if (getRversion() < "3.1.0") {
# R-3.0 does not contain these functions, set them here to prevent installation failure
# (required for extension of the <mic> class)
cospi <- function(...) 1
sinpi <- function(...) 1
tanpi <- function(...) 1
}
if (getRversion() < "3.2.0") {
anyNA <- function(x, recursive = FALSE) {
if (isTRUE(recursive) && (is.list(x) || is.pairlist(x))) {
return(any(rapply(x, anyNA, how = "unlist", recursive = FALSE)))
}
any(is.na(x))
}
dir.exists <- function(paths) {
x <- base::file.info(paths)$isdir
!is.na(x) & x
}
file.size <- function(...) {
file.info(...)$size
}
file.mtime <- function(...) {
file.info(...)$mtime
}
isNamespaceLoaded <- function(pkg) {
pkg %in% loadedNamespaces()
}
lengths <- function(x, use.names = TRUE) {
vapply(x, length, FUN.VALUE = NA_integer_, USE.NAMES = use.names)
}
}
if (getRversion() < "3.3.0") {
strrep <- function(x, times) {
x <- as.character(x)
if (length(x) == 0L) {
return(x)
@ -1450,8 +1456,8 @@ strrep <- function(x, times) {
}
paste0(replicate(times, x), collapse = "")
}, list(x = x, times = times), MoreArgs = list()), use.names = FALSE)
}
trimws <- function(x, which = c("both", "left", "right"), whitespace = "[ \t\r\n]") {
}
trimws <- function(x, which = c("both", "left", "right"), whitespace = "[ \t\r\n]") {
which <- match.arg(which)
mysub <- function(re, x) sub(re, "", x, perl = TRUE)
switch(which,
@ -1459,40 +1465,28 @@ trimws <- function(x, which = c("both", "left", "right"), whitespace = "[ \t\r\n
right = mysub(paste0(whitespace, "+$"), x),
both = mysub(paste0(whitespace, "+$"), mysub(paste0("^", whitespace, "+"), x))
)
}
}
isFALSE <- function(x) {
if (getRversion() < "3.4.2") {
isFALSE <- function(x) {
is.logical(x) && length(x) == 1L && !is.na(x) && !x
}
}
deparse1 <- function(expr, collapse = " ", width.cutoff = 500L, ...) {
paste(deparse(expr, width.cutoff, ...), collapse = collapse)
}
file.size <- function(...) {
file.info(...)$size
}
file.mtime <- function(...) {
file.info(...)$mtime
}
str2lang <- function(s) {
if (getRversion() < "3.6.0") {
str2lang <- function(s) {
stopifnot(length(s) == 1L)
ex <- parse(text = s, keep.source = FALSE)
stopifnot(length(ex) == 1L)
ex[[1L]]
}
isNamespaceLoaded <- function(pkg) {
pkg %in% loadedNamespaces()
}
lengths <- function(x, use.names = TRUE) {
vapply(x, length, FUN.VALUE = NA_integer_, USE.NAMES = use.names)
}
}
if (getRversion() < "3.1") {
# R-3.0 does not contain these functions, set them here to prevent installation failure
# (required for extension of the <mic> class)
cospi <- function(...) 1
sinpi <- function(...) 1
tanpi <- function(...) 1
}
dir.exists <- function(paths) {
x <- base::file.info(paths)$isdir
!is.na(x) & x
if (getRversion() < "4.0.0") {
deparse1 <- function(expr, collapse = " ", width.cutoff = 500L, ...) {
paste(deparse(expr, width.cutoff, ...), collapse = collapse)
}
}
# nolint end

2
R/aa_helper_pm_functions.R
View File

@ -601,7 +601,7 @@ pm_near <- function(x, y, tol = .Machine$double.eps^0.5) {
pm_pull <- function(.data, var = -1) {
var_deparse <- pm_deparse_var(var)
col_names <- colnames(.data)
if (!(var_deparse %in% col_names) & grepl("^[[:digit:]]+L|[[:digit:]]", var_deparse)) {
if (!(var_deparse %in% col_names) && grepl("^[[:digit:]]+L|[[:digit:]]", var_deparse)) {
var <- as.integer(gsub("L", "", var_deparse))
var <- pm_if_else(var < 1L, rev(col_names)[abs(var)], col_names[var])
} else if (var_deparse %in% col_names) {

41
R/ab.R
View File

@ -91,8 +91,6 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
meet_criteria(flag_multiple_results, allow_class = "logical", has_length = 1)
meet_criteria(info, allow_class = "logical", has_length = 1)
check_dataset_integrity()
if (is.ab(x)) {
return(x)
}
@ -109,7 +107,6 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
x_bak <- x
x <- toupper(x)
x_nonNA <- x[!is.na(x)]
# remove diacritics
x <- iconv(x, from = "UTF-8", to = "ASCII//TRANSLIT")
@ -128,7 +125,7 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
x_unknown_ATCs <- character(0)
note_if_more_than_one_found <- function(found, index, from_text) {
if (initial_search == TRUE & isTRUE(length(from_text) > 1)) {
if (initial_search == TRUE && isTRUE(length(from_text) > 1)) {
abnames <- ab_name(from_text, tolower = TRUE, initial_search = FALSE)
if (ab_name(found[1L], language = NULL) %like% "(clavulanic acid|avibactam)") {
abnames <- abnames[!abnames %in% c("clavulanic acid", "avibactam")]
@ -165,7 +162,7 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
x_new[known_codes_cid] <- AB_lookup$ab[match(x[known_codes_cid], AB_lookup$cid)]
already_known <- known_names | known_codes_ab | known_codes_atc | known_codes_cid
if (initial_search == TRUE & sum(already_known) < length(x)) {
if (initial_search == TRUE && sum(already_known) < length(x)) {
progress <- progress_ticker(n = sum(!already_known), n_min = 25, print = info) # start if n >= 25
on.exit(close(progress))
}
@ -175,10 +172,10 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
progress$tick()
}
if (is.na(x[i]) | is.null(x[i])) {
if (is.na(x[i]) || is.null(x[i])) {
next
}
if (identical(x[i], "") |
if (identical(x[i], "") ||
# prevent "bacteria" from coercing to TMP, since Bacterial is a brand name of it:
identical(tolower(x[i]), "bacteria")) {
x_unknown <- c(x_unknown, x_bak[x[i] == x_bak_clean][1])
@ -211,7 +208,7 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
AB_lookup$generalised_loinc,
function(s) x[i] %in% s
))
found <- antibiotics$ab[loinc_found == TRUE]
found <- AMR::antibiotics$ab[loinc_found == TRUE]
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
@ -222,7 +219,7 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
AB_lookup$generalised_synonyms,
function(s) x[i] %in% s
))
found <- antibiotics$ab[synonym_found == TRUE]
found <- AMR::antibiotics$ab[synonym_found == TRUE]
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
@ -232,9 +229,9 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
abbr_found <- unlist(lapply(
AB_lookup$generalised_abbreviations,
# require at least 2 characters for abbreviations
function(s) x[i] %in% s & nchar(x[i]) >= 2
function(s) x[i] %in% s && nchar(x[i]) >= 2
))
found <- antibiotics$ab[abbr_found == TRUE]
found <- AMR::antibiotics$ab[abbr_found == TRUE]
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
@ -281,14 +278,14 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
}
# try if name starts with it
found <- antibiotics[which(AB_lookup$generalised_name %like% paste0("^", x_spelling)), "ab", drop = TRUE]
found <- AMR::antibiotics[which(AB_lookup$generalised_name %like% paste0("^", x_spelling)), "ab", drop = TRUE]
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
# try if name ends with it
found <- antibiotics[which(AB_lookup$generalised_name %like% paste0(x_spelling, "$")), "ab", drop = TRUE]
if (nchar(x[i]) >= 4 & length(found) > 0) {
found <- AMR::antibiotics[which(AB_lookup$generalised_name %like% paste0(x_spelling, "$")), "ab", drop = TRUE]
if (nchar(x[i]) >= 4 && length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
@ -298,7 +295,7 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
AB_lookup$generalised_synonyms,
function(s) any(s %like% paste0("^", x_spelling))
))
found <- antibiotics$ab[synonym_found == TRUE]
found <- AMR::antibiotics$ab[synonym_found == TRUE]
if (length(found) > 0) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
@ -312,16 +309,16 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
# try by removing all spaces
if (x[i] %like% " ") {
found <- suppressWarnings(as.ab(gsub(" +", "", x[i], perl = TRUE), initial_search = FALSE))
if (length(found) > 0 & !is.na(found)) {
if (length(found) > 0 && !is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
}
# try by removing all spaces and numbers
if (x[i] %like% " " | x[i] %like% "[0-9]") {
if (x[i] %like% " " || x[i] %like% "[0-9]") {
found <- suppressWarnings(as.ab(gsub("[ 0-9]", "", x[i], perl = TRUE), initial_search = FALSE))
if (length(found) > 0 & !is.na(found)) {
if (length(found) > 0 && !is.na(found)) {
x_new[i] <- note_if_more_than_one_found(found, i, from_text)
next
}
@ -477,7 +474,7 @@ as.ab <- function(x, flag_multiple_results = TRUE, info = interactive(), ...) {
x_unknown <- c(x_unknown, x_bak[x[i] == x_bak_clean][1])
}
if (initial_search == TRUE & sum(already_known) < length(x)) {
if (initial_search == TRUE && sum(already_known) < length(x)) {
close(progress)
}
@ -566,7 +563,7 @@ as.data.frame.ab <- function(x, ...) {
"[<-.ab" <- function(i, j, ..., value) {
y <- NextMethod()
attributes(y) <- attributes(i)
return_after_integrity_check(y, "antimicrobial code", antibiotics$ab)
return_after_integrity_check(y, "antimicrobial code", AMR::antibiotics$ab)
}
#' @method [[<- ab
#' @export
@ -574,7 +571,7 @@ as.data.frame.ab <- function(x, ...) {
"[[<-.ab" <- function(i, j, ..., value) {
y <- NextMethod()
attributes(y) <- attributes(i)
return_after_integrity_check(y, "antimicrobial code", antibiotics$ab)
return_after_integrity_check(y, "antimicrobial code", AMR::antibiotics$ab)
}
#' @method c ab
#' @export
@ -583,7 +580,7 @@ c.ab <- function(...) {
x <- list(...)[[1L]]
y <- NextMethod()
attributes(y) <- attributes(x)
return_after_integrity_check(y, "antimicrobial code", antibiotics$ab)
return_after_integrity_check(y, "antimicrobial code", AMR::antibiotics$ab)
}
#' @method unique ab

16
R/ab_from_text.R
View File

@ -120,21 +120,21 @@ ab_from_text <- function(text,
if (type %like% "(drug|ab|anti)") {
translate_ab <- get_translate_ab(translate_ab)
if (isTRUE(thorough_search) |
(isTRUE(is.null(thorough_search)) & max(vapply(FUN.VALUE = double(1), text_split_all, length), na.rm = TRUE) <= 3)) {
if (isTRUE(thorough_search) ||
(isTRUE(is.null(thorough_search)) && max(vapply(FUN.VALUE = double(1), text_split_all, length), na.rm = TRUE) <= 3)) {
text_split_all <- text_split_all[nchar(text_split_all) >= 4 & grepl("[a-z]+", text_split_all)]
result <- lapply(text_split_all, function(text_split) {
progress$tick()
suppressWarnings(
out <- as.ab(text_split, ...)
as.ab(text_split, ...)
)
})
} else {
# no thorough search
abbr <- unlist(antibiotics$abbreviations)
abbr <- unlist(AMR::antibiotics$abbreviations)
abbr <- abbr[nchar(abbr) >= 4]
names_atc <- substr(c(antibiotics$name, antibiotics$atc), 1, 5)
synonyms <- unlist(antibiotics$synonyms)
names_atc <- substr(c(AMR::antibiotics$name, AMR::antibiotics$atc), 1, 5)
synonyms <- unlist(AMR::antibiotics$synonyms)
synonyms <- synonyms[nchar(synonyms) >= 4]
# regular expression must not be too long, so split synonyms in two:
synonyms_part1 <- synonyms[seq_len(0.5 * length(synonyms))]
@ -149,7 +149,7 @@ ab_from_text <- function(text,
result <- lapply(text_split_all, function(text_split) {
progress$tick()
suppressWarnings(
out <- as.ab(
as.ab(
unique(c(
text_split[text_split %like_case% to_regex(abbr)],
text_split[text_split %like_case% to_regex(names_atc)],
@ -176,7 +176,7 @@ ab_from_text <- function(text,
}
})
} else if (type %like% "dos") {
text_split_all <- strsplit(text, " ")
text_split_all <- strsplit(text, " ", fixed = TRUE)
result <- lapply(text_split_all, function(text_split) {
text_split <- text_split[text_split %like% "^[0-9]{2,}(/[0-9]+)?[a-z]*$"]
# only left part of "/", like 500 in "500/125"

26
R/ab_property.R
View File

@ -125,7 +125,7 @@
#' }
ab_name <- function(x, language = get_AMR_locale(), tolower = FALSE, ...) {
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(tolower, allow_class = "logical", has_length = 1)
x <- translate_into_language(ab_validate(x = x, property = "name", ...), language = language, only_affect_ab_names = TRUE)
@ -168,7 +168,7 @@ ab_tradenames <- function(x, ...) {
#' @export
ab_group <- function(x, language = get_AMR_locale(), ...) {
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
translate_into_language(ab_validate(x = x, property = "group", ...), language = language, only_affect_ab_names = TRUE)
}
@ -208,7 +208,7 @@ ab_atc <- function(x, only_first = FALSE, ...) {
#' @export
ab_atc_group1 <- function(x, language = get_AMR_locale(), ...) {
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
translate_into_language(ab_validate(x = x, property = "atc_group1", ...), language = language, only_affect_ab_names = TRUE)
}
@ -216,7 +216,7 @@ ab_atc_group1 <- function(x, language = get_AMR_locale(), ...) {
#' @export
ab_atc_group2 <- function(x, language = get_AMR_locale(), ...) {
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
translate_into_language(ab_validate(x = x, property = "atc_group2", ...), language = language, only_affect_ab_names = TRUE)
}
@ -289,7 +289,7 @@ ab_ddd_units <- function(x, administration = "oral", ...) {
#' @export
ab_info <- function(x, language = get_AMR_locale(), ...) {
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
x <- as.ab(x, ...)
list(
@ -334,7 +334,7 @@ ab_url <- function(x, open = FALSE, ...) {
}
if (open == TRUE) {
if (length(u) > 1 & !is.na(u[1L])) {
if (length(u) > 1 && !is.na(u[1L])) {
warning_("in `ab_url()`: only the first URL will be opened, as `browseURL()` only suports one string.")
}
if (!is.na(u[1L])) {
@ -348,7 +348,7 @@ ab_url <- function(x, open = FALSE, ...) {
#' @export
ab_property <- function(x, property = "name", language = get_AMR_locale(), ...) {
meet_criteria(x, allow_NA = TRUE)
meet_criteria(property, is_in = colnames(antibiotics), has_length = 1)
meet_criteria(property, is_in = colnames(AMR::antibiotics), has_length = 1)
meet_criteria(language, is_in = c(LANGUAGES_SUPPORTED, ""), has_length = 1, allow_NULL = TRUE, allow_NA = TRUE)
translate_into_language(ab_validate(x = x, property = property, ...), language = language)
}
@ -358,8 +358,8 @@ ab_property <- function(x, property = "name", language = get_AMR_locale(), ...)
#' @export
set_ab_names <- function(data, ..., property = "name", language = get_AMR_locale(), snake_case = NULL) {
meet_criteria(data, allow_class = c("data.frame", "character"))
meet_criteria(property, is_in = colnames(antibiotics), has_length = 1, ignore.case = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
meet_criteria(property, is_in = colnames(AMR::antibiotics), has_length = 1, ignore.case = TRUE)
language <- validate_language(language)
meet_criteria(snake_case, allow_class = "logical", has_length = 1, allow_NULL = TRUE)
x_deparsed <- deparse(substitute(data))
@ -422,7 +422,7 @@ set_ab_names <- function(data, ..., property = "name", language = get_AMR_locale
x <- tolower(gsub("[^a-zA-Z0-9]+", "_", x))
}
if (any(duplicated(x))) {
if (anyDuplicated(x)) {
# very hacky way of adding the index to each duplicate
# so "Amoxicillin", "Amoxicillin", "Amoxicillin"
# will be "Amoxicillin", "Amoxicillin_2", "Amoxicillin_3"
@ -433,7 +433,7 @@ set_ab_names <- function(data, ..., property = "name", language = get_AMR_locale
if (length(dups) > 1) {
# there are duplicates
dup_add_int <- dups[2:length(dups)]
x[dup_add_int] <<- paste0(x[dup_add_int], "_", c(2:length(dups)))
x[dup_add_int] <<- paste0(x[dup_add_int], "_", 2:length(dups))
}
}
))
@ -448,15 +448,13 @@ set_ab_names <- function(data, ..., property = "name", language = get_AMR_locale
}
ab_validate <- function(x, property, ...) {
check_dataset_integrity()
if (tryCatch(all(x[!is.na(x)] %in% AB_lookup$ab), error = function(e) FALSE)) {
# special case for ab_* functions where class is already <ab>
x <- AB_lookup[match(x, AB_lookup$ab), property, drop = TRUE]
} else {
# try to catch an error when inputting an invalid argument
# so the 'call.' can be set to FALSE
tryCatch(x[1L] %in% antibiotics[1, property, drop = TRUE],
tryCatch(x[1L] %in% AMR::antibiotics[1, property, drop = TRUE],
error = function(e) stop(e$message, call. = FALSE)
)

26
R/ab_selectors.R
View File

@ -420,8 +420,8 @@ administrable_per_os <- function(only_rsi_columns = FALSE, ...) {
info = FALSE, only_rsi_columns = only_rsi_columns,
sort = FALSE, fn = "administrable_per_os"
)
agents_all <- antibiotics[which(!is.na(antibiotics$oral_ddd)), "ab", drop = TRUE]
agents <- antibiotics[which(antibiotics$ab %in% ab_in_data & !is.na(antibiotics$oral_ddd)), "ab", drop = TRUE]
agents_all <- AMR::antibiotics[which(!is.na(AMR::antibiotics$oral_ddd)), "ab", drop = TRUE]
agents <- AMR::antibiotics[which(AMR::antibiotics$ab %in% ab_in_data & !is.na(AMR::antibiotics$oral_ddd)), "ab", drop = TRUE]
agents <- ab_in_data[ab_in_data %in% agents]
message_agent_names(
function_name = "administrable_per_os",
@ -458,8 +458,8 @@ administrable_iv <- function(only_rsi_columns = FALSE, ...) {
info = FALSE, only_rsi_columns = only_rsi_columns,
sort = FALSE, fn = "administrable_iv"
)
agents_all <- antibiotics[which(!is.na(antibiotics$iv_ddd)), "ab", drop = TRUE]
agents <- antibiotics[which(antibiotics$ab %in% ab_in_data & !is.na(antibiotics$iv_ddd)), "ab", drop = TRUE]
agents_all <- AMR::antibiotics[which(!is.na(AMR::antibiotics$iv_ddd)), "ab", drop = TRUE]
agents <- AMR::antibiotics[which(AMR::antibiotics$ab %in% ab_in_data & !is.na(AMR::antibiotics$iv_ddd)), "ab", drop = TRUE]
agents <- ab_in_data[ab_in_data %in% agents]
message_agent_names(
function_name = "administrable_iv",
@ -539,7 +539,7 @@ ab_select_exec <- function(function_name,
)
# untreatable drugs
if (only_treatable == TRUE) {
untreatable <- antibiotics[which(antibiotics$name %like% "-high|EDTA|polysorbate|macromethod|screening|/nacubactam"), "ab", drop = TRUE]
untreatable <- AMR::antibiotics[which(AMR::antibiotics$name %like% "-high|EDTA|polysorbate|macromethod|screening|/nacubactam"), "ab", drop = TRUE]
if (any(untreatable %in% names(ab_in_data))) {
if (message_not_thrown_before(function_name, "ab_class", "untreatable", entire_session = TRUE)) {
warning_(
@ -782,16 +782,16 @@ find_ab_names <- function(ab_group, n = 3) {
ab_group <- gsub("[^a-zA-Z|0-9]", ".*", ab_group)
# try popular first, they have DDDs
drugs <- antibiotics[which((!is.na(antibiotics$iv_ddd) | !is.na(antibiotics$oral_ddd)) &
antibiotics$name %unlike% " " &
antibiotics$group %like% ab_group &
antibiotics$ab %unlike% "[0-9]$"), ]$name
drugs <- AMR::antibiotics[which((!is.na(AMR::antibiotics$iv_ddd) | !is.na(AMR::antibiotics$oral_ddd)) &
AMR::antibiotics$name %unlike% " " &
AMR::antibiotics$group %like% ab_group &
AMR::antibiotics$ab %unlike% "[0-9]$"), ]$name
if (length(drugs) < n) {
# now try it all
drugs <- antibiotics[which((antibiotics$group %like% ab_group |
antibiotics$atc_group1 %like% ab_group |
antibiotics$atc_group2 %like% ab_group) &
antibiotics$ab %unlike% "[0-9]$"), ]$name
drugs <- antibiotics[which((AMR::antibiotics$group %like% ab_group |
AMR::antibiotics$atc_group1 %like% ab_group |
AMR::antibiotics$atc_group2 %like% ab_group) &
AMR::antibiotics$ab %unlike% "[0-9]$"), ]$name
}
if (length(drugs) == 0) {
return("??")

2
R/amr.R
View File

@ -34,7 +34,7 @@
#' This package is fully independent of any other \R package and works on Windows, macOS and Linux with all versions of \R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. This \R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.
#'
#' This package can be used for:
#' - Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life and List of Prokaryotic names with Standing in Nomenclature
#' - Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF)
#' - Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines
#' - Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records
#' - Determining first isolates to be used for AMR data analysis

8
R/atc_online.R
View File

@ -94,9 +94,7 @@ atc_online_property <- function(atc_code,
html_text <- import_fn("html_text", "rvest")
read_html <- import_fn("read_html", "xml2")
check_dataset_integrity()
if (!all(atc_code %in% unlist(antibiotics$atc))) {
if (!all(atc_code %in% unlist(AMR::antibiotics$atc))) {
atc_code <- as.character(ab_atc(atc_code, only_first = TRUE))
}
@ -183,7 +181,7 @@ atc_online_property <- function(atc_code,
# ATC and name are only in first row
returnvalue[i] <- out[1, property, drop = TRUE]
} else {
if (!"adm.r" %in% colnames(out) | is.na(out[1, "adm.r", drop = TRUE])) {
if (!"adm.r" %in% colnames(out) || is.na(out[1, "adm.r", drop = TRUE])) {
returnvalue[i] <- NA
next
} else {
@ -197,7 +195,7 @@ atc_online_property <- function(atc_code,
}
}
if (property == "groups" & length(returnvalue) == 1) {
if (property == "groups" && length(returnvalue) == 1) {
returnvalue <- returnvalue[[1]]
}

28
R/bug_drug_combinations.R
View File

@ -178,7 +178,7 @@ format.bug_drug_combinations <- function(x,
...) {
meet_criteria(x, allow_class = "data.frame")
meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
meet_criteria(combine_IR, allow_class = "logical", has_length = 1)
@ -196,10 +196,10 @@ format.bug_drug_combinations <- function(x,
x <- data.frame(
mo = gsub("(.*)%%(.*)", "\\1", names(idx)),
ab = gsub("(.*)%%(.*)", "\\2", names(idx)),
S = sapply(idx, function(i) sum(x$S[i], na.rm = TRUE)),
I = sapply(idx, function(i) sum(x$I[i], na.rm = TRUE)),
R = sapply(idx, function(i) sum(x$R[i], na.rm = TRUE)),
total = sapply(idx, function(i) {
S = vapply(FUN.VALUE = double(1), idx, function(i) sum(x$S[i], na.rm = TRUE)),
I = vapply(FUN.VALUE = double(1), idx, function(i) sum(x$I[i], na.rm = TRUE)),
R = vapply(FUN.VALUE = double(1), idx, function(i) sum(x$R[i], na.rm = TRUE)),
total = vapply(FUN.VALUE = double(1), idx, function(i) {
sum(x$S[i], na.rm = TRUE) +
sum(x$I[i], na.rm = TRUE) +
sum(x$R[i], na.rm = TRUE)
@ -214,7 +214,7 @@ format.bug_drug_combinations <- function(x,
if (remove_intrinsic_resistant == TRUE) {
x <- subset(x, R != total)
}
if (combine_SI == TRUE | combine_IR == FALSE) {
if (combine_SI == TRUE || combine_IR == FALSE) {
x$isolates <- x$R
} else {
x$isolates <- x$R + x$I
@ -224,13 +224,13 @@ format.bug_drug_combinations <- function(x,
format <- tolower(format)
ab_txt <- rep(format, length(ab))
for (i in seq_len(length(ab_txt))) {
ab_txt[i] <- gsub("ab", as.character(as.ab(ab[i])), ab_txt[i])
ab_txt[i] <- gsub("cid", ab_cid(ab[i]), ab_txt[i])
ab_txt[i] <- gsub("group", ab_group(ab[i], language = language), ab_txt[i])
ab_txt[i] <- gsub("atc_group1", ab_atc_group1(ab[i], language = language), ab_txt[i])
ab_txt[i] <- gsub("atc_group2", ab_atc_group2(ab[i], language = language), ab_txt[i])
ab_txt[i] <- gsub("atc", ab_atc(ab[i], only_first = TRUE), ab_txt[i])
ab_txt[i] <- gsub("name", ab_name(ab[i], language = language), ab_txt[i])
ab_txt[i] <- gsub("ab", as.character(as.ab(ab[i])), ab_txt[i], fixed = TRUE)
ab_txt[i] <- gsub("cid", ab_cid(ab[i]), ab_txt[i], fixed = TRUE)
ab_txt[i] <- gsub("group", ab_group(ab[i], language = language), ab_txt[i], fixed = TRUE)
ab_txt[i] <- gsub("atc_group1", ab_atc_group1(ab[i], language = language), ab_txt[i], fixed = TRUE)
ab_txt[i] <- gsub("atc_group2", ab_atc_group2(ab[i], language = language), ab_txt[i], fixed = TRUE)
ab_txt[i] <- gsub("atc", ab_atc(ab[i], only_first = TRUE), ab_txt[i], fixed = TRUE)
ab_txt[i] <- gsub("name", ab_name(ab[i], language = language), ab_txt[i], fixed = TRUE)
ab_txt[i]
}
ab_txt
@ -317,7 +317,7 @@ format.bug_drug_combinations <- function(x,
}
if (remove_intrinsic_resistant == TRUE) {
y <- y[, !vapply(FUN.VALUE = logical(1), y, function(col) all(col %like% "100", na.rm = TRUE) & !any(is.na(col))), drop = FALSE]
y <- y[, !vapply(FUN.VALUE = logical(1), y, function(col) all(col %like% "100", na.rm = TRUE) & !anyNA(col)), drop = FALSE]
}
rownames(y) <- NULL

145
R/catalogue_of_life.R
View File

@ -1,145 +0,0 @@
# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Data Analysis for R #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2022 Berends MS, Luz CF et al. #
# Developed at the University of Groningen, the Netherlands, in #
# collaboration with non-profit organisations Certe Medical #
# Diagnostics & Advice, and University Medical Center Groningen. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
format_included_data_number <- function(data) {
if (is.data.frame(data)) {
n <- nrow(data)
} else {
n <- length(unique(data))
}
if (n > 10000) {
rounder <- -3 # round on thousands
} else if (n > 1000) {
rounder <- -2 # round on hundreds
} else {
rounder <- -1 # round on tens
}
paste0("~", format(round(n, rounder), decimal.mark = ".", big.mark = ","))
}
#' The Catalogue of Life
#'
#' This package contains the complete taxonomic tree (last updated: `r CATALOGUE_OF_LIFE$yearmonth_LPSN`) of almost all microorganisms from the authoritative and comprehensive Catalogue of Life (CoL), supplemented with data from the List of Prokaryotic names with Standing in Nomenclature (LPSN).
#' @section Catalogue of Life:
#' \if{html}{\figure{logo_col.png}{options: height="40" style=margin-bottom:"5"} \cr}
#' This package contains the complete taxonomic tree of almost all microorganisms (`r format_included_data_number(microorganisms)` species) from the authoritative and comprehensive Catalogue of Life (CoL, <http://www.catalogueoflife.org>). The CoL is the most comprehensive and authoritative global index of species currently available. Nonetheless, we supplemented the CoL data with data from the List of Prokaryotic names with Standing in Nomenclature (LPSN, [lpsn.dsmz.de](https://lpsn.dsmz.de)). This supplementation is needed until the [CoL+ project](https://github.com/CatalogueOfLife/general) is finished, which we await.
#'
#' [Click here][catalogue_of_life] for more information about the included taxa. Check which versions of the CoL and LPSN were included in this package with [catalogue_of_life_version()].
#' @section Included Taxa:
#' Included are:
#' - All `r format_included_data_number(microorganisms[which(microorganisms$kingdom %in% c("Archeae", "Bacteria", "Chromista", "Protozoa")), , drop = FALSE])` (sub)species from the kingdoms of Archaea, Bacteria, Chromista and Protozoa
#' - All `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Fungi" & microorganisms$order %in% c("Eurotiales", "Microascales", "Mucorales", "Onygenales", "Pneumocystales", "Saccharomycetales", "Schizosaccharomycetales", "Tremellales")), , drop = FALSE])` (sub)species from these orders of the kingdom of Fungi: Eurotiales, Microascales, Mucorales, Onygenales, Pneumocystales, Saccharomycetales, Schizosaccharomycetales and Tremellales, as well as `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Fungi" & !microorganisms$order %in% c("Eurotiales", "Microascales", "Mucorales", "Onygenales", "Pneumocystales", "Saccharomycetales", "Schizosaccharomycetales", "Tremellales")), ])` other fungal (sub)species. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package and including everything would tremendously slow down our algorithms too. By only including the aforementioned taxonomic orders, the most relevant fungi are covered (such as all species of *Aspergillus*, *Candida*, *Cryptococcus*, *Histplasma*, *Pneumocystis*, *Saccharomyces* and *Trichophyton*).
#' - All `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Animalia"), , drop = FALSE])` (sub)species from `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Animalia"), "genus", drop = TRUE])` other relevant genera from the kingdom of Animalia (such as *Strongyloides* and *Taenia*)
#' - All `r format_included_data_number(microorganisms.old)` previously accepted names of all included (sub)species (these were taxonomically renamed)
#' - The complete taxonomic tree of all included (sub)species: from kingdom to subspecies
#' - The responsible author(s) and year of scientific publication
#'
#' The Catalogue of Life (<http://www.catalogueoflife.org>) is the most comprehensive and authoritative global index of species currently available. It holds essential information on the names, relationships and distributions of over 1.9 million species. The Catalogue of Life is used to support the major biodiversity and conservation information services such as the Global Biodiversity Information Facility (GBIF), Encyclopedia of Life (EoL) and the International Union for Conservation of Nature Red List. It is recognised by the Convention on Biological Diversity as a significant component of the Global Taxonomy Initiative and a contribution to Target 1 of the Global Strategy for Plant Conservation.
#'
#' The syntax used to transform the original data to a cleansed \R format, can be found here: <https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R>.
#' @name catalogue_of_life
#' @rdname catalogue_of_life
#' @seealso Data set [microorganisms] for the actual data. \cr
#' Function [as.mo()] to use the data for intelligent determination of microorganisms.
#' @examples
#' # Get version info of included data set
#' catalogue_of_life_version()
#'
#'
#' # Get a note when a species was renamed
#' mo_shortname("Chlamydophila psittaci")
#'
#' # Get any property from the entire taxonomic tree for all included species
#' mo_class("Escherichia coli")
#'
#' mo_family("Escherichia coli")
#'
#' mo_gramstain("Escherichia coli") # based on kingdom and phylum, see ?mo_gramstain
#'
#' mo_ref("Escherichia coli")
#'
#' # Do not get mistaken - this package is about microorganisms
#' mo_kingdom("C. elegans")
#' mo_name("C. elegans")
NULL
#' Version info of included Catalogue of Life
#'
#' This function returns information about the included data from the Catalogue of Life.
#' @seealso [microorganisms]
#' @details For LPSN, see [microorganisms].
#' @return a [list], which prints in pretty format
#' @inheritSection catalogue_of_life Catalogue of Life
#' @export
catalogue_of_life_version <- function() {
check_dataset_integrity()
# see the `CATALOGUE_OF_LIFE` list in R/globals.R
lst <- list(
CoL =
list(
version = gsub("{year}", CATALOGUE_OF_LIFE$year, CATALOGUE_OF_LIFE$version, fixed = TRUE),
url = gsub("{year}", CATALOGUE_OF_LIFE$year, CATALOGUE_OF_LIFE$url_CoL, fixed = TRUE),
n = nrow(pm_filter(microorganisms, source == "CoL"))
),
LPSN =
list(
version = "List of Prokaryotic names with Standing in Nomenclature",
url = CATALOGUE_OF_LIFE$url_LPSN,
yearmonth = CATALOGUE_OF_LIFE$yearmonth_LPSN,
n = nrow(pm_filter(microorganisms, source == "LPSN"))
),
total_included =
list(
n_total_species = nrow(microorganisms),
n_total_synonyms = nrow(microorganisms.old)
)
)
set_clean_class(lst,
new_class = c("catalogue_of_life_version", "list")
)
}
#' @method print catalogue_of_life_version
#' @export
#' @noRd
print.catalogue_of_life_version <- function(x, ...) {
cat(paste0(
font_bold("Included in this AMR package (v", utils::packageDescription("AMR")$Version, ") are:\n\n", collapse = ""),
font_underline(x$CoL$version), "\n",
" Available at: ", font_blue(x$CoL$url), "\n",
" Number of included microbial species: ", format(x$CoL$n, big.mark = ","), "\n",
font_underline(paste0(
x$LPSN$version, " (",
x$LPSN$yearmonth, ")"
)), "\n",
" Available at: ", font_blue(x$LPSN$url), "\n",
" Number of included bacterial species: ", format(x$LPSN$n, big.mark = ","), "\n\n",
"=> Total number of species included: ", format(x$total_included$n_total_species, big.mark = ","), "\n",
"=> Total number of synonyms included: ", format(x$total_included$n_total_synonyms, big.mark = ","), "\n\n",
"See for more info ", font_grey_bg("`?microorganisms`"), " and ", font_grey_bg("`?catalogue_of_life`"), ".\n"
))
}

12
R/custom_eucast_rules.R
View File

@ -207,11 +207,11 @@ print.custom_eucast_rules <- function(x, ...) {
if (is.na(rule$result_value)) {
val <- font_red("<NA>")
} else if (rule$result_value == "R") {
val <- font_rsi_R_bg(font_black(" R "))
val <- font_red_bg(" R ")
} else if (rule$result_value == "S") {
val <- font_rsi_S_bg(font_black(" S "))
val <- font_green_bg(" S ")
} else {
val <- font_rsi_I_bg(font_black(" I "))
val <- font_orange_bg(" I ")
}
agents <- paste0(
font_blue(ab_name(rule$result_group, language = NULL, tolower = TRUE),
@ -248,9 +248,9 @@ format_custom_query_rule <- function(query, colours = has_colour()) {
query <- gsub(" %in% ", font_black(" is one of "), query, fixed = TRUE)
query <- gsub(" %like% ", font_black(" resembles "), query, fixed = TRUE)
if (colours == TRUE) {
query <- gsub('"R"', font_rsi_R_bg(font_black(" R ")), query, fixed = TRUE)
query <- gsub('"S"', font_rsi_S_bg(font_black(" S ")), query, fixed = TRUE)
query <- gsub('"I"', font_rsi_I_bg(font_black(" I ")), query, fixed = TRUE)
query <- gsub('"R"', font_red_bg(" R "), query, fixed = TRUE)
query <- gsub('"S"', font_green_bg(" S "), query, fixed = TRUE)
query <- gsub('"I"', font_orange_bg(" I "), query, fixed = TRUE)
}
# replace the black colour 'stops' with blue colour 'starts'
query <- gsub("\033[39m", "\033[34m", as.character(query), fixed = TRUE)

77
R/data.R
View File

@ -74,81 +74,67 @@
#' Data Set with `r format(nrow(microorganisms), big.mark = ",")` Microorganisms
#'
#' A data set containing the full microbial taxonomy (**last updated: `r CATALOGUE_OF_LIFE$yearmonth_LPSN`**) of `r nr2char(length(unique(microorganisms$kingdom[!microorganisms$kingdom %like% "unknown"])))` kingdoms from the Catalogue of Life (CoL) and the List of Prokaryotic names with Standing in Nomenclature (LPSN). MO codes can be looked up using [as.mo()].
#' @inheritSection catalogue_of_life Catalogue of Life
#' A data set containing the full microbial taxonomy (**last updated: `r documentation_date(max(TAXONOMY_VERSION$GBIF$accessed_date, TAXONOMY_VERSION$LPSN$accessed_date))`**) of `r nr2char(length(unique(microorganisms$kingdom[!microorganisms$kingdom %like% "unknown"])))` kingdoms from the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF). This data set is the backbone of this `AMR` package. MO codes can be looked up using [as.mo()].
#' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms), big.mark = ",")` observations and `r ncol(microorganisms)` variables:
#' - `mo`\cr ID of microorganism as used by this package
#' - `fullname`\cr Full name, like `"Escherichia coli"`
#' - `fullname`\cr Full name, like `"Escherichia coli"`. For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon.
#' - `status` \cr Status of the taxon, either `r vector_or(microorganisms$status)`
#' - `kingdom`, `phylum`, `class`, `order`, `family`, `genus`, `species`, `subspecies`\cr Taxonomic rank of the microorganism
#' - `rank`\cr Text of the taxonomic rank of the microorganism, like `"species"` or `"genus"`
#' - `ref`\cr Author(s) and year of concerning scientific publication
#' - `species_id`\cr ID of the species as used by the Catalogue of Life
#' - `rank`\cr Text of the taxonomic rank of the microorganism, such as `"species"` or `"genus"`
#' - `ref`\cr Author(s) and year of related scientific publication. This contains only the *first surname* and year of the *latest* authors, e.g. "Wallis *et al.* 2006 *emend.* Smith and Jones 2018" becomes "Smith *et al.*, 2018". This field is directly retrieved from the source specified in the column `source`. Moreover, accents were removed to comply with CRAN that only allows ASCII characters, e.g. "V`r "\u00e1\u0148ov\u00e1"`" becomes "Vanova".
#' - `lpsn`\cr Identifier ('Record number') of the List of Prokaryotic names with Standing in Nomenclature (LPSN). This will be the first/highest LPSN identifier to keep one identifier per row. For example, *Acetobacter ascendens* has LPSN Record number 7864 and 11011. Only the first is available in the `microorganisms` data set.
#' - `lpsn_parent`\cr LPSN identifier of the parent taxon
#' - `lpsn_renamed_to`\cr LPSN identifier of the currently valid taxon
#' - `gbif`\cr Identifier ('taxonID') of the Global Biodiversity Information Facility (GBIF)
#' - `gbif_parent`\cr GBIF identifier of the parent taxon
#' - `gbif_renamed_to`\cr GBIF identifier of the currently valid taxon
#' - `source`\cr Either `r vector_or(microorganisms$source)` (see *Source*)
#' - `prevalence`\cr Prevalence of the microorganism, see [as.mo()]
#' - `snomed`\cr Systematized Nomenclature of Medicine (SNOMED) code of the microorganism, according to the `r SNOMED_VERSION$current_source` (see *Source*). Use [mo_snomed()] to retrieve it quickly, see [mo_property()].
#' - `snomed`\cr Systematized Nomenclature of Medicine (SNOMED) code of the microorganism, version of `r documentation_date(TAXONOMY_VERSION$SNOMED$accessed_date)` (see *Source*). Use [mo_snomed()] to retrieve it quickly, see [mo_property()].
#' @details
#' Please note that entries are only based on the Catalogue of Life and the LPSN (see below). Since these sources incorporate entries based on (recent) publications in the International Journal of Systematic and Evolutionary Microbiology (IJSEM), it can happen that the year of publication is sometimes later than one might expect.
#' Please note that entries are only based on the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF) (see below). Since these sources incorporate entries based on (recent) publications in the International Journal of Systematic and Evolutionary Microbiology (IJSEM), it can happen that the year of publication is sometimes later than one might expect.
#'
#' For example, *Staphylococcus pettenkoferi* was described for the first time in Diagnostic Microbiology and Infectious Disease in 2002 (\doi{10.1016/s0732-8893(02)00399-1}), but it was not before 2007 that a publication in IJSEM followed (\doi{10.1099/ijs.0.64381-0}). Consequently, the `AMR` package returns 2007 for `mo_year("S. pettenkoferi")`.
#'
#' @section Included Taxa:
#' Included taxonomic data are:
#' - All `r format_included_data_number(microorganisms[which(microorganisms$kingdom %in% c("Archeae", "Bacteria", "Protozoa")), , drop = FALSE])` (sub)species from the kingdoms of Archaea, Bacteria and Protozoa
#' - All `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Fungi"), , drop = FALSE])` (sub)species from `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Fungi"), "order", drop = TRUE])` relevant orders of the kingdom of Fungi. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package and including everything would tremendously slow down our algorithms too. By only including relevant taxonomic orders, the most relevant fungi are covered (such as all species of *Aspergillus*, *Candida*, *Cryptococcus*, *Histplasma*, *Pneumocystis*, *Saccharomyces* and *Trichophyton*).
#' - All `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Animalia"), , drop = FALSE])` (sub)species from `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Animalia"), "genus", drop = TRUE])` other relevant genera from the kingdom of Animalia (such as *Strongyloides* and *Taenia*)
#' - All `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Plantae"), , drop = FALSE])` (sub)species from `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Animalia"), "genus", drop = TRUE])` other relevant genera from the kingdom of Animalia (such as *Strongyloides* and *Taenia*)
#' - All `r format_included_data_number(microorganisms[which(microorganisms$status != "accepted"), , drop = FALSE])` previously accepted names of all included (sub)species (these were taxonomically renamed)
#' - The complete taxonomic tree of all included (sub)species: from kingdom to subspecies
#' - The identifier of the parent taxons
#' - The responsible author(s) and year of scientific publication
#'
#' ## Manual additions
#' For convenience, some entries were added manually:
#'
#' - 11 entries of *Streptococcus* (beta-haemolytic: groups A, B, C, D, F, G, H, K and unspecified; other: viridans, milleri)
#' - 2 entries of *Staphylococcus* (coagulase-negative (CoNS) and coagulase-positive (CoPS))
#' - 3 entries of *Trichomonas* (*T. vaginalis*, and its family and genus)
#' - 4 entries of *Toxoplasma* (*T. gondii*, and its order, family and genus)
#' - 1 entry of *Candida* (*C. krusei*), that is not (yet) in the Catalogue of Life
#' - 1 entry of *Blastocystis* (*B. hominis*), although it officially does not exist (Noel *et al.* 2005, PMID 15634993)
#' - 1 entry of *Moraxella* (*M. catarrhalis*), which was formally named *Branhamella catarrhalis* (Catlin, 1970) though this change was never accepted within the field of clinical microbiology
#' - 5 other 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast and unknown fungus)
#' - 6 families under the Enterobacterales order, according to Adeolu *et al.* (2016, PMID 27620848), that are not (yet) in the Catalogue of Life
#'
#' The syntax used to transform the original data to a cleansed \R format, can be found here: <https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R>.
#'
#' ## Direct download
#' Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
#' @section About the Records from LPSN (see *Source*):
#' LPSN is the main source for bacteriological taxonomy of this `AMR` package.
#'
#' The List of Prokaryotic names with Standing in Nomenclature (LPSN) provides comprehensive information on the nomenclature of prokaryotes. LPSN is a free to use service founded by Jean P. Euzeby in 1997 and later on maintained by Aidan C. Parte.
#'
#' As of February 2020, the regularly augmented LPSN database at DSMZ is the basis of the new LPSN service. The new database was implemented for the Type-Strain Genome Server and augmented in 2018 to store all kinds of nomenclatural information. Data from the previous version of LPSN and from the Prokaryotic Nomenclature Up-to-date (PNU) service were imported into the new system. PNU had been established in 1993 as a service of the Leibniz Institute DSMZ, and was curated by Norbert Weiss, Manfred Kracht and Dorothea Gleim.
#' @source
#' `r gsub("{year}", CATALOGUE_OF_LIFE$year, CATALOGUE_OF_LIFE$version, fixed = TRUE)` as currently implemented in this `AMR` package:
#' * `r TAXONOMY_VERSION$LPSN$citation` Accessed from <`r TAXONOMY_VERSION$LPSN$url`> on `r documentation_date(TAXONOMY_VERSION$LPSN$accessed_date)`.
#'
#' * Annual Checklist (public online taxonomic database), <http://www.catalogueoflife.org>
#' * `r TAXONOMY_VERSION$GBIF$citation` Accessed from <`r TAXONOMY_VERSION$GBIF$url`> on `r documentation_date(TAXONOMY_VERSION$GBIF$accessed_date)`.
#'
#' List of Prokaryotic names with Standing in Nomenclature (`r CATALOGUE_OF_LIFE$yearmonth_LPSN`) as currently implemented in this `AMR` package:
#'
#' * Parte, A.C., Sarda Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. and Goker, M. (2020). List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ. International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; \doi{10.1099/ijsem.0.004332}
#' * Parte, A.C. (2018). LPSN - List of Prokaryotic names with Standing in Nomenclature (bacterio.net), 20 years on. International Journal of Systematic and Evolutionary Microbiology, 68, 1825-1829; \doi{10.1099/ijsem.0.002786}
#' * Parte, A.C. (2014). LPSN - List of Prokaryotic names with Standing in Nomenclature. Nucleic Acids Research, 42, Issue D1, D613-D616; \doi{10.1093/nar/gkt1111}
#' * Euzeby, J.P. (1997). List of Bacterial Names with Standing in Nomenclature: a Folder Available on the Internet. International Journal of Systematic Bacteriology, 47, 590-592; \doi{10.1099/00207713-47-2-590}
#'
#' `r SNOMED_VERSION$current_source` as currently implemented in this `AMR` package:
#'
#' * Retrieved from the `r SNOMED_VERSION$title`, OID `r SNOMED_VERSION$current_oid`, version `r SNOMED_VERSION$current_version`; url: <`r SNOMED_VERSION$url`>
#' * `r TAXONOMY_VERSION$SNOMED$citation` URL: <`r TAXONOMY_VERSION$SNOMED$url`>
#' @seealso [as.mo()], [mo_property()], [microorganisms.codes], [intrinsic_resistant]
#' @examples
#' microorganisms
"microorganisms"
#' Data Set with Previously Accepted Taxonomic Names
#'
#' A data set containing old (previously valid or accepted) taxonomic names according to the Catalogue of Life. This data set is used internally by [as.mo()].
#' @inheritSection catalogue_of_life Catalogue of Life
#' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms.old), big.mark = ",")` observations and `r ncol(microorganisms.old)` variables:
#' - `fullname`\cr Old full taxonomic name of the microorganism
#' - `fullname_new`\cr New full taxonomic name of the microorganism
#' - `ref`\cr Author(s) and year of concerning scientific publication
#' - `prevalence`\cr Prevalence of the microorganism, see [as.mo()]
#' @details
#' Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
#' @source Catalogue of Life: Annual Checklist (public online taxonomic database), <http://www.catalogueoflife.org> (check included annual version with [catalogue_of_life_version()]).
#'
#' Parte, A.C. (2018). LPSN - List of Prokaryotic names with Standing in Nomenclature (bacterio.net), 20 years on. International Journal of Systematic and Evolutionary Microbiology, 68, 1825-1829; \doi{10.1099/ijsem.0.002786}
#' @seealso [as.mo()] [mo_property()] [microorganisms]
#' @examples
#' microorganisms.old
"microorganisms.old"
#' Data Set with `r format(nrow(microorganisms.codes), big.mark = ",")` Common Microorganism Codes
#'
#' A data set containing commonly used codes for microorganisms, from laboratory systems and WHONET. Define your own with [set_mo_source()]. They will all be searched when using [as.mo()] and consequently all the [`mo_*`][mo_property()] functions.
@ -157,7 +143,6 @@
#' - `mo`\cr ID of the microorganism in the [microorganisms] data set
#' @details
#' Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
#' @inheritSection catalogue_of_life Catalogue of Life
#' @seealso [as.mo()] [microorganisms]
#' @examples
#' microorganisms.codes

4
R/disk.R
View File

@ -79,7 +79,7 @@ as.disk <- function(x, na.rm = FALSE) {
# heavily based on cleaner::clean_double():
clean_double2 <- function(x, remove = "[^0-9.,-]", fixed = FALSE) {
x <- gsub(",", ".", x)
x <- gsub(",", ".", x, fixed = TRUE)
# remove ending dot/comma
x <- gsub("[,.]$", "", x)
# only keep last dot/comma
@ -131,7 +131,7 @@ all_valid_disks <- function(x) {
x_disk <- tryCatch(suppressWarnings(as.disk(x[!is.na(x)])),
error = function(e) NA
)
!any(is.na(x_disk)) && !all(is.na(x))
!anyNA(x_disk) && !all(is.na(x))
}
#' @rdname as.disk

25
R/eucast_rules.R
View File

@ -46,14 +46,13 @@ format_eucast_version_nr <- function(version, markdown = TRUE) {
))
}
}
vector_and(txt, quotes = FALSE)
}
#' Apply EUCAST Rules
#'
#' @description
#' Apply rules for clinical breakpoints and intrinsic resistance as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, <https://eucast.org>), see *Source*. Use [eucast_dosage()] to get a [data.frame] with advised dosages of a certain bug-drug combination, which is based on the [dosage] data set.
#' Apply rules for clinical breakpoints and intrinsic resistance as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, <https://www.eucast.org>), see *Source*. Use [eucast_dosage()] to get a [data.frame] with advised dosages of a certain bug-drug combination, which is based on the [dosage] data set.
#'
#' To improve the interpretation of the antibiogram before EUCAST rules are applied, some non-EUCAST rules can applied at default, see *Details*.
#' @param x a data set with antibiotic columns, such as `amox`, `AMX` and `AMC`
@ -73,7 +72,7 @@ format_eucast_version_nr <- function(version, markdown = TRUE) {
#' **Note:** This function does not translate MIC values to RSI values. Use [as.rsi()] for that. \cr
#' **Note:** When ampicillin (AMP, J01CA01) is not available but amoxicillin (AMX, J01CA04) is, the latter will be used for all rules where there is a dependency on ampicillin. These drugs are interchangeable when it comes to expression of antimicrobial resistance. \cr
#'
#' The file containing all EUCAST rules is located here: <https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv>. **Note:** Old taxonomic names are replaced with the current taxonomy where applicable. For example, *Ochrobactrum anthropi* was renamed to *Brucella anthropi* in 2020; the original EUCAST rules v3.1 and v3.2 did not yet contain this new taxonomic name. The file used as input for this `AMR` package contains the taxonomy updated until [`r CATALOGUE_OF_LIFE$yearmonth_LPSN`][catalogue_of_life()].
#' The file containing all EUCAST rules is located here: <https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv>. **Note:** Old taxonomic names are replaced with the current taxonomy where applicable. For example, *Ochrobactrum anthropi* was renamed to *Brucella anthropi* in 2020; the original EUCAST rules v3.1 and v3.2 did not yet contain this new taxonomic name. The `AMR` package contains the full microbial taxonomy updated until `r documentation_date(max(TAXONOMY_VERSION$GBIF$accessed_date, TAXONOMY_VERSION$LPSN$accessed_date))`, see [microorganisms].
#'
#' ## Custom Rules
#'
@ -199,8 +198,6 @@ eucast_rules <- function(x,
x_deparsed <- "your_data"
}
check_dataset_integrity()
breakpoints_info <- EUCAST_VERSION_BREAKPOINTS[[which(as.double(names(EUCAST_VERSION_BREAKPOINTS)) == version_breakpoints)]]
expertrules_info <- EUCAST_VERSION_EXPERT_RULES[[which(as.double(names(EUCAST_VERSION_EXPERT_RULES)) == version_expertrules)]]
@ -334,7 +331,7 @@ eucast_rules <- function(x,
strsplit(",") %pm>%
unlist() %pm>%
trimws() %pm>%
vapply(FUN.VALUE = character(1), function(x) if (x %in% antibiotics$ab) ab_name(x, language = NULL, tolower = TRUE, fast_mode = TRUE) else x) %pm>%
vapply(FUN.VALUE = character(1), function(x) if (x %in% AMR::antibiotics$ab) ab_name(x, language = NULL, tolower = TRUE, fast_mode = TRUE) else x) %pm>%
sort() %pm>%
paste(collapse = ", ")
x <- gsub("_", " ", x, fixed = TRUE)
@ -423,13 +420,13 @@ eucast_rules <- function(x,
# big speed gain! only analyse unique rows:
pm_distinct(`.rowid`, .keep_all = TRUE) %pm>%
as.data.frame(stringsAsFactors = FALSE)
x[, col_mo] <- as.mo(as.character(x[, col_mo, drop = TRUE]))
x[, col_mo] <- as.mo(as.character(x[, col_mo, drop = TRUE]), info = info)
# rename col_mo to prevent interference with joined columns
colnames(x)[colnames(x) == col_mo] <- ".col_mo"
col_mo <- ".col_mo"
# join to microorganisms data set
x <- left_join_microorganisms(x, by = col_mo, suffix = c("_oldcols", ""))
x$gramstain <- mo_gramstain(x[, col_mo, drop = TRUE], language = NULL)
x$gramstain <- mo_gramstain(x[, col_mo, drop = TRUE], language = NULL, info = FALSE)
x$genus_species <- trimws(paste(x$genus, x$species))
if (info == TRUE & NROW(x) > 10000) {
message_(" OK.", add_fn = list(font_green, font_bold), as_note = FALSE)
@ -437,11 +434,11 @@ eucast_rules <- function(x,
if (any(x$genus == "Staphylococcus", na.rm = TRUE)) {
all_staph <- MO_lookup[which(MO_lookup$genus == "Staphylococcus"), , drop = FALSE]
all_staph$CNS_CPS <- suppressWarnings(mo_name(all_staph$mo, Becker = "all", language = NULL))
all_staph$CNS_CPS <- suppressWarnings(mo_name(all_staph$mo, Becker = "all", language = NULL, info = FALSE))
}
if (any(x$genus == "Streptococcus", na.rm = TRUE)) {
all_strep <- MO_lookup[which(MO_lookup$genus == "Streptococcus"), , drop = FALSE]
all_strep$Lancefield <- suppressWarnings(mo_name(all_strep$mo, Lancefield = TRUE, language = NULL))
all_strep$Lancefield <- suppressWarnings(mo_name(all_strep$mo, Lancefield = TRUE, language = NULL, info = FALSE))
}
n_added <- 0
@ -461,10 +458,10 @@ eucast_rules <- function(x,
))
))
}
ab_enzyme <- subset(antibiotics, name %like% "/")[, c("ab", "name"), drop = FALSE]
ab_enzyme <- subset(AMR::antibiotics, name %like% "/")[, c("ab", "name"), drop = FALSE]
colnames(ab_enzyme) <- c("enzyme_ab", "enzyme_name")
ab_enzyme$base_name <- gsub("^([a-zA-Z0-9]+).*", "\\1", ab_enzyme$enzyme_name)
ab_enzyme$base_ab <- antibiotics[match(ab_enzyme$base_name, antibiotics$name), "ab", drop = TRUE]
ab_enzyme$base_ab <- AMR::antibiotics[match(ab_enzyme$base_name, AMR::antibiotics$name), "ab", drop = TRUE]
ab_enzyme <- subset(ab_enzyme, !is.na(base_ab))
# make ampicillin and amoxicillin interchangable
ampi <- subset(ab_enzyme, base_ab == "AMX")
@ -1073,11 +1070,11 @@ edit_rsi <- function(x,
)
txt_error <- function() {
if (info == TRUE) cat("", font_red_bg(font_white(" ERROR ")), "\n\n")
if (info == TRUE) cat("", font_red_bg(" ERROR "), "\n\n")
}
txt_warning <- function() {
if (warned == FALSE) {
if (info == TRUE) cat(" ", font_rsi_I_bg(" WARNING "), sep = "")
if (info == TRUE) cat(" ", font_orange_bg(" WARNING "), sep = "")
}
warned <<- TRUE
}

40
R/first_isolate.R
View File

@ -174,18 +174,6 @@ first_isolate <- function(x = NULL,
include_unknown = FALSE,
include_untested_rsi = TRUE,
...) {
dots <- unlist(list(...))
if (length(dots) != 0) {
# backwards compatibility with old arguments
dots.names <- names(dots)
if ("filter_specimen" %in% dots.names) {
specimen_group <- dots[which(dots.names == "filter_specimen")]
}
if ("col_keyantibiotics" %in% dots.names) {
col_keyantimicrobials <- dots[which(dots.names == "col_keyantibiotics")]
}
}
if (is_null_or_grouped_tbl(x)) {
# when `x` is left blank, auto determine it (get_current_data() also contains dplyr::cur_data_all())
# is also fix for using a grouped df as input (a dot as first argument)
@ -248,10 +236,10 @@ first_isolate <- function(x = NULL,
FUN = function(x) any(as.character(x[1:10000]) %in% c("R", "S", "I"), na.rm = TRUE),
USE.NAMES = FALSE
))
if (method == "phenotype-based" & !any_col_contains_rsi) {
if (method == "phenotype-based" && !any_col_contains_rsi) {
method <- "episode-based"
}
if (info == TRUE & message_not_thrown_before("first_isolate", "method")) {
if (info == TRUE && message_not_thrown_before("first_isolate", "method")) {
message_(paste0(
"Determining first isolates ",
ifelse(method %in% c("episode-based", "phenotype-based"),
@ -288,14 +276,14 @@ first_isolate <- function(x = NULL,
} else if (method == "episode-based") {
col_keyantimicrobials <- NULL
} else if (method == "phenotype-based") {
if (missing(type) & !is.null(col_keyantimicrobials)) {
if (missing(type) && !is.null(col_keyantimicrobials)) {
# type = "points" is default, but not set explicitly, while col_keyantimicrobials is
type <- "keyantimicrobials"
}
if (type == "points") {
x$keyantimicrobials <- all_antimicrobials(x, only_rsi_columns = FALSE)
col_keyantimicrobials <- "keyantimicrobials"
} else if (type == "keyantimicrobials" & is.null(col_keyantimicrobials)) {
} else if (type == "keyantimicrobials" && is.null(col_keyantimicrobials)) {
col_keyantimicrobials <- search_type_in_df(x = x, type = "keyantimicrobials", info = info)
if (is.null(col_keyantimicrobials)) {
# still not found as a column, create it ourselves
@ -325,7 +313,7 @@ first_isolate <- function(x = NULL,
}
# -- specimen
if (is.null(col_specimen) & !is.null(specimen_group)) {
if (is.null(col_specimen) && !is.null(specimen_group)) {
col_specimen <- search_type_in_df(x = x, type = "specimen", info = info)
}
@ -361,7 +349,7 @@ first_isolate <- function(x = NULL,
testcodes_exclude <- NULL
}
# remove testcodes
if (!is.null(testcodes_exclude) & info == TRUE & message_not_thrown_before("first_isolate", "excludingtestcodes")) {
if (!is.null(testcodes_exclude) && info == TRUE && message_not_thrown_before("first_isolate", "excludingtestcodes")) {
message_("Excluding test codes: ", vector_and(testcodes_exclude, quotes = TRUE),
add_fn = font_black,
as_note = FALSE
@ -375,7 +363,7 @@ first_isolate <- function(x = NULL,
# filter on specimen group and keyantibiotics when they are filled in
if (!is.null(specimen_group)) {
check_columns_existance(col_specimen, x)
if (info == TRUE & message_not_thrown_before("first_isolate", "excludingspecimen")) {
if (info == TRUE && message_not_thrown_before("first_isolate", "excludingspecimen")) {
message_("Excluding other than specimen group '", specimen_group, "'",
add_fn = font_black,
as_note = FALSE
@ -418,7 +406,7 @@ first_isolate <- function(x = NULL,
}
# speed up - return immediately if obvious
if (abs(row.start) == Inf | abs(row.end) == Inf) {
if (abs(row.start) == Inf || abs(row.end) == Inf) {
if (info == TRUE) {
message_("=> Found ", font_bold("no isolates"),
add_fn = font_black,
@ -455,7 +443,7 @@ first_isolate <- function(x = NULL,
# Analysis of first isolate ----
if (!is.null(col_keyantimicrobials)) {
if (info == TRUE & message_not_thrown_before("first_isolate", "type")) {
if (info == TRUE && message_not_thrown_before("first_isolate", "type")) {
if (type == "keyantimicrobials") {
message_("Basing inclusion on key antimicrobials, ",
ifelse(ignore_I == FALSE, "not ", ""),
@ -474,11 +462,7 @@ first_isolate <- function(x = NULL,
}
}
x$other_pat_or_mo <- ifelse(x$newvar_patient_id == pm_lag(x$newvar_patient_id) &
x$newvar_genus_species == pm_lag(x$newvar_genus_species),
FALSE,
TRUE
)
x$other_pat_or_mo <- !(x$newvar_patient_id == pm_lag(x$newvar_patient_id) & x$newvar_genus_species == pm_lag(x$newvar_genus_species))
x$episode_group <- paste(x$newvar_patient_id, x$newvar_genus_species)
x$more_than_episode_ago <- unlist(lapply(split(
@ -570,7 +554,7 @@ first_isolate <- function(x = NULL,
}
# handle empty microorganisms
if (any(x$newvar_mo == "UNKNOWN", na.rm = TRUE) & info == TRUE) {
if (any(x$newvar_mo == "UNKNOWN", na.rm = TRUE) && info == TRUE) {
message_(
ifelse(include_unknown == TRUE, "Included ", "Excluded "),
format(sum(x$newvar_mo == "UNKNOWN", na.rm = TRUE),
@ -582,7 +566,7 @@ first_isolate <- function(x = NULL,
x[which(x$newvar_mo == "UNKNOWN"), "newvar_first_isolate"] <- include_unknown
# exclude all NAs
if (any(is.na(x$newvar_mo)) & info == TRUE) {
if (anyNA(x$newvar_mo) && info == TRUE) {
message_(
"Excluded ", format(sum(is.na(x$newvar_mo), na.rm = TRUE),
decimal.mark = decimal.mark, big.mark = big.mark

2
R/g.test.R
View File

@ -149,7 +149,7 @@ g.test <- function(x,
paste(DNAME2, collapse = "\n")
)
}
if (any(x < 0) || any(is.na((x)))) { # this last one was anyNA, but only introduced in R 3.1.0
if (any(x < 0) || anyNA(x)) {
stop("all entries of 'x' must be nonnegative and finite")
}
if ((n <- sum(x)) == 0) {

4
R/ggplot_pca.R
View File

@ -232,7 +232,7 @@ ggplot_pca <- function(x,
}
# Overlay a concentration ellipse if there are groups
if (!is.null(df.u$groups) & !is.null(ell) & isTRUE(ellipse)) {
if (!is.null(df.u$groups) && !is.null(ell) && isTRUE(ellipse)) {
g <- g + ggplot2::geom_path(
data = ell,
ggplot2::aes(colour = groups, group = groups),
@ -319,7 +319,7 @@ pca_calculations <- function(pca_model,
error = function(e) NULL
)
}
if (!is.null(groups) & is.null(labels)) {
if (!is.null(groups) && is.null(labels)) {
# turn them around
labels <- groups
groups <- NULL

8
R/ggplot_rsi.R
View File

@ -211,7 +211,7 @@ ggplot_rsi <- function(data,
meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
meet_criteria(combine_IR, allow_class = "logical", has_length = 1)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(nrow, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive = TRUE, is_finite = TRUE)
meet_criteria(colours, allow_class = c("character", "logical"))
meet_criteria(datalabels, allow_class = "logical", has_length = 1)
@ -311,12 +311,12 @@ geom_rsi <- function(position = NULL,
meet_criteria(fill, allow_class = "character", has_length = 1)
meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
meet_criteria(combine_IR, allow_class = "logical", has_length = 1)
y <- "value"
if (missing(position) | is.null(position)) {
if (missing(position) || is.null(position)) {
position <- "fill"
}
@ -500,7 +500,7 @@ labels_rsi_count <- function(position = NULL,
meet_criteria(x, allow_class = "character", has_length = 1)
meet_criteria(translate_ab, allow_class = c("character", "logical"), has_length = 1, allow_NA = TRUE)
meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(combine_SI, allow_class = "logical", has_length = 1)
meet_criteria(combine_IR, allow_class = "logical", has_length = 1)
meet_criteria(datalabels.size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)

12
R/guess_ab_col.R
View File

@ -59,7 +59,7 @@ guess_ab_col <- function(x = NULL, search_string = NULL, verbose = FALSE, only_r
meet_criteria(verbose, allow_class = "logical", has_length = 1)
meet_criteria(only_rsi_columns, allow_class = "logical", has_length = 1)
if (is.null(x) & is.null(search_string)) {
if (is.null(x) && is.null(search_string)) {
return(as.name("guess_ab_col"))
} else {
meet_criteria(search_string, allow_class = "character", has_length = 1, allow_NULL = FALSE)
@ -205,7 +205,7 @@ get_column_abx <- function(x,
dots <- dots[!vapply(FUN.VALUE = logical(1), dots, is.data.frame)]
if (length(dots) > 0) {
newnames <- suppressWarnings(as.ab(names(dots), info = FALSE))
if (any(is.na(newnames))) {
if (anyNA(newnames)) {
if (info == TRUE) {
message_(" WARNING", add_fn = list(font_yellow, font_bold), as_note = FALSE)
}
@ -236,7 +236,7 @@ get_column_abx <- function(x,
}
if (length(out) == 0) {
if (info == TRUE & all_okay == TRUE) {
if (info == TRUE && all_okay == TRUE) {
message_("No columns found.")
}
pkg_env$get_column_abx.call <- unique_call_id(entire_session = FALSE, match_fn = fn)
@ -262,7 +262,7 @@ get_column_abx <- function(x,
message_(" WARNING.", add_fn = list(font_yellow, font_bold), as_note = FALSE)
}
for (i in seq_len(length(out))) {
if (verbose == TRUE & !names(out[i]) %in% names(duplicates)) {
if (verbose == TRUE && !names(out[i]) %in% names(duplicates)) {
message_(
"Using column '", font_bold(out[i]), "' as input for ", names(out)[i],
" (", ab_name(names(out)[i], tolower = TRUE, language = NULL), ")."
@ -300,7 +300,7 @@ get_column_abx <- function(x,
}
if (!is.null(soft_dependencies)) {
soft_dependencies <- unique(soft_dependencies)
if (info == TRUE & !all(soft_dependencies %in% names(out))) {
if (info == TRUE && !all(soft_dependencies %in% names(out))) {
# missing a soft dependency may lower the reliability
missing <- soft_dependencies[!soft_dependencies %in% names(out)]
missing_msg <- vector_and(paste0(
@ -325,7 +325,7 @@ get_column_abx <- function(x,
get_ab_from_namespace <- function(x, cols_ab) {
# cols_ab comes from get_column_abx()
x <- trimws(unique(toupper(unlist(strsplit(x, ",")))))
x <- trimws(unique(toupper(unlist(strsplit(x, ",", fixed = TRUE)))))
x_new <- character()
for (val in x) {
if (paste0("AB_", val) %in% ls(envir = asNamespace("AMR"))) {

2
R/italicise_taxonomy.R
View File

@ -62,7 +62,7 @@ italicise_taxonomy <- function(string, type = c("markdown", "ansi")) {
FUN.VALUE = character(1),
string,
function(s) {
s_split <- unlist(strsplit(s, " "))
s_split <- unlist(strsplit(s, " ", fixed = TRUE))
search_strings <- gsub("[^a-zA-Z-]", "", s_split)

2
R/join_microorganisms.R
View File

@ -123,8 +123,6 @@ anti_join_microorganisms <- function(x, by = NULL, ...) {
}
join_microorganisms <- function(type, x, by, suffix, ...) {
check_dataset_integrity()
if (!is.data.frame(x)) {
if (pkg_is_available("tibble", also_load = FALSE)) {
x <- import_fn("tibble", "tibble")(mo = x)

11
R/key_antimicrobials.R
View File

@ -175,8 +175,8 @@ key_antimicrobials <- function(x = NULL,
values <- cols[names(cols) %in% values]
values_new_length <- length(values)
if (values_new_length < values_old_length &
any(filter, na.rm = TRUE) &
if (values_new_length < values_old_length &&
any(filter, na.rm = TRUE) &&
message_not_thrown_before("key_antimicrobials", name)) {
warning_(
"in `key_antimicrobials()`: ",
@ -305,7 +305,7 @@ antimicrobials_equal <- function(y,
stop_ifnot(length(y) == length(z), "length of `y` and `z` must be equal")
key2rsi <- function(val) {
val <- strsplit(val, "")[[1L]]
val <- strsplit(val, "", fixed = TRUE)[[1L]]
val.int <- rep(NA_real_, length(val))
val.int[val == "S"] <- 1
val.int[val == "I"] <- 2
@ -347,8 +347,8 @@ antimicrobials_equal <- function(y,
all(a == b, na.rm = TRUE)
}
}
out <- unlist(mapply(
FUN = determine_equality,
out <- unlist(Map(
f = determine_equality,
y,
z,
MoreArgs = list(
@ -356,7 +356,6 @@ antimicrobials_equal <- function(y,
points_threshold = points_threshold,
ignore_I = ignore_I
),
SIMPLIFY = FALSE,
USE.NAMES = FALSE
))
out[is.na(y) | is.na(z)] <- NA

5
R/like.R
View File

@ -102,14 +102,13 @@ like <- function(x, pattern, ignore.case = TRUE) {
)
}
unlist(
mapply(
FUN = grepl,
Map(
f = grepl,
x = x,
pattern = pattern,
fixed = fixed,
perl = !fixed,
MoreArgs = list(ignore.case = FALSE),
SIMPLIFY = FALSE,
USE.NAMES = FALSE
)
)

81
R/mdro.R
View File

@ -190,15 +190,13 @@ mdro <- function(x = NULL,
meet_criteria(verbose, allow_class = "logical", has_length = 1)
meet_criteria(only_rsi_columns, allow_class = "logical", has_length = 1)
check_dataset_integrity()
info.bak <- info
# don't thrown info's more than once per call
if (isTRUE(info)) {
info <- message_not_thrown_before("mdro")
}
if (interactive() & verbose == TRUE & info == TRUE) {
if (interactive() && isTRUE(verbose) && isTRUE(info)) {
txt <- paste0(
"WARNING: In Verbose mode, the mdro() function does not return the MDRO results, but instead returns a data set in logbook form with extensive info about which isolates would be MDRO-positive, or why they are not.",
"\n\nThis may overwrite your existing data if you use e.g.:",
@ -217,7 +215,7 @@ mdro <- function(x = NULL,
}
group_msg <- ""
if (info.bak == TRUE) {
if (isTRUE(info.bak)) {
# print group name if used in dplyr::group_by()
cur_group <- import_fn("cur_group", "dplyr", error_on_fail = FALSE)
if (!is.null(cur_group)) {
@ -255,7 +253,7 @@ mdro <- function(x = NULL,
if (is.list(guideline)) {
# Custom MDRO guideline ---------------------------------------------------
stop_ifnot(inherits(guideline, "custom_mdro_guideline"), "use `custom_mdro_guideline()` to create custom guidelines")
if (info == TRUE) {
if (isTRUE(info)) {
txt <- paste0(
"Determining MDROs based on custom rules",
ifelse(isTRUE(attributes(guideline)$as_factor),
@ -268,7 +266,7 @@ mdro <- function(x = NULL,
cat(txt, "\n", sep = "")
}
x <- run_custom_mdro_guideline(df = x, guideline = guideline, info = info)
if (info.bak == TRUE) {
if (isTRUE(info.bak)) {
cat(group_msg)
if (sum(!is.na(x$MDRO)) == 0) {
cat(word_wrap(font_bold(paste0("=> Found 0 MDROs since no isolates are covered by the custom guideline"))))
@ -282,7 +280,7 @@ mdro <- function(x = NULL,
))))
}
}
if (verbose == TRUE) {
if (isTRUE(verbose)) {
return(x[, c(
"row_number",
"MDRO",
@ -319,7 +317,7 @@ mdro <- function(x = NULL,
if (is.null(col_mo)) {
col_mo <- search_type_in_df(x = x, type = "mo", info = info)
}
if (is.null(col_mo) & guideline$code == "tb") {
if (is.null(col_mo) && guideline$code == "tb") {
message_(
"No column found as input for `col_mo`, ",
font_bold(paste0("assuming all rows contain ", font_italic("Mycobacterium tuberculosis"), "."))
@ -614,9 +612,9 @@ mdro <- function(x = NULL,
...
)
}
if (!"AMP" %in% names(cols_ab) & "AMX" %in% names(cols_ab)) {
if (!"AMP" %in% names(cols_ab) && "AMX" %in% names(cols_ab)) {
# ampicillin column is missing, but amoxicillin is available
if (info == TRUE) {
if (isTRUE(info)) {
message_("Using column '", cols_ab[names(cols_ab) == "AMX"], "' as input for ampicillin since many EUCAST rules depend on it.")
}
cols_ab <- c(cols_ab, c(AMP = unname(cols_ab[names(cols_ab) == "AMX"])))
@ -767,14 +765,14 @@ mdro <- function(x = NULL,
stop_if(guideline$code == "tb" & length(abx_tb) == 0, "no antimycobacterials found in data set")
# nolint end
if (combine_SI == TRUE) {
if (isTRUE(combine_SI)) {
search_result <- "R"
} else {
search_result <- c("R", "I")
}
if (info == TRUE) {
if (combine_SI == TRUE) {
if (isTRUE(info)) {
if (isTRUE(combine_SI)) {
cat(font_red("\nOnly results with 'R' are considered as resistance. Use `combine_SI = FALSE` to also consider 'I' as resistance.\n"))
} else {
cat(font_red("\nResults with 'R' or 'I' are considered as resistance. Use `combine_SI = TRUE` to only consider 'R' as resistance.\n"))
@ -819,7 +817,7 @@ mdro <- function(x = NULL,
trans_tbl <- function(to, rows, cols, any_all) {
cols <- cols[!ab_missing(cols)]
cols <- cols[!is.na(cols)]
if (length(rows) > 0 & length(cols) > 0) {
if (length(rows) > 0 && length(cols) > 0) {
x[, cols] <- as.data.frame(lapply(
x[, cols, drop = FALSE],
function(col) as.rsi(col)
@ -836,7 +834,7 @@ mdro <- function(x = NULL,
function(y) y %in% search_result
)
paste(sort(c(
unlist(strsplit(x[row, "columns_nonsusceptible", drop = TRUE], ", ")),
unlist(strsplit(x[row, "columns_nonsusceptible", drop = TRUE], ", ", fixed = TRUE)),
names(cols_nonsus)[cols_nonsus]
)),
collapse = ", "
@ -871,7 +869,7 @@ mdro <- function(x = NULL,
}
trans_tbl2 <- function(txt, rows, lst) {
if (info == TRUE) {
if (isTRUE(info)) {
message_(txt, "...", appendLF = FALSE, as_note = FALSE)
}
if (length(rows) > 0) {
@ -896,7 +894,7 @@ mdro <- function(x = NULL,
}
)
if (verbose == TRUE) {
if (isTRUE(verbose)) {
x[rows, "columns_nonsusceptible"] <<- vapply(
FUN.VALUE = character(1),
rows,
@ -929,7 +927,7 @@ mdro <- function(x = NULL,
x[which(row_filter), "classes_affected"] <<- 999
}
if (info == TRUE) {
if (isTRUE(info)) {
message_(" OK.", add_fn = list(font_green, font_bold), as_note = FALSE)
}
}
@ -951,21 +949,21 @@ mdro <- function(x = NULL,
# (see header 'Approaches to Creating Definitions for MDR, XDR and PDR' in paper)
# take amoxicillin if ampicillin is unavailable
if (is.na(AMP) & !is.na(AMX)) {
if (verbose == TRUE) {
if (is.na(AMP) && !is.na(AMX)) {
if (isTRUE(verbose)) {
message_("Filling ampicillin (AMP) results with amoxicillin (AMX) results")
}
AMP <- AMX
}
# take ceftriaxone if cefotaxime is unavailable and vice versa
if (is.na(CRO) & !is.na(CTX)) {
if (verbose == TRUE) {
if (is.na(CRO) && !is.na(CTX)) {
if (isTRUE(verbose)) {
message_("Filling ceftriaxone (CRO) results with cefotaxime (CTX) results")
}
CRO <- CTX
}
if (is.na(CTX) & !is.na(CRO)) {
if (verbose == TRUE) {
if (is.na(CTX) && !is.na(CRO)) {
if (isTRUE(verbose)) {
message_("Filling cefotaxime (CTX) results with ceftriaxone (CRO) results")
}
CTX <- CRO
@ -1156,7 +1154,7 @@ mdro <- function(x = NULL,
# now set MDROs:
# MDR (=2): >=3 classes affected
x[which(x$classes_affected >= 3), "MDRO"] <- 2
if (verbose == TRUE) {
if (isTRUE(verbose)) {
x[which(x$classes_affected >= 3), "reason"] <- paste0(
"at least 3 classes contain R",
ifelse(!isTRUE(combine_SI), " or I", ""), ": ",
@ -1167,7 +1165,7 @@ mdro <- function(x = NULL,
# XDR (=3): all but <=2 classes affected
x[which((x$classes_in_guideline - x$classes_affected) <= 2), "MDRO"] <- 3
if (verbose == TRUE) {
if (isTRUE(verbose)) {
x[which(x$MDRO == 3), "reason"] <- paste0(
"less than 3 classes remain susceptible (", x$classes_in_guideline[which((x$classes_in_guideline - x$classes_affected) <= 2)] - x$classes_affected[which(x$MDRO == 3)],
" out of ", x$classes_in_guideline[which(x$MDRO == 3)], " classes)"
@ -1176,7 +1174,7 @@ mdro <- function(x = NULL,
# PDR (=4): all agents are R
x[which(x$classes_affected == 999 & x$classes_in_guideline == x$classes_available), "MDRO"] <- 4
if (verbose == TRUE) {
if (isTRUE(verbose)) {
x[which(x$MDRO == 4), "reason"] <- paste(
"all antibiotics in all",
x$classes_in_guideline[which(x$MDRO == 4)],
@ -1187,7 +1185,7 @@ mdro <- function(x = NULL,
# not enough classes available
x[which(x$MDRO %in% c(1, 3) & x$classes_available < floor(x$classes_in_guideline * pct_required_classes)), "MDRO"] <- -1
if (verbose == TRUE) {
if (isTRUE(verbose)) {
x[which(x$MDRO == -1), "reason"] <- paste0(
"not enough classes available: ", x$classes_available[which(x$MDRO == -1)],
" of required ", (floor(x$classes_in_guideline * pct_required_classes))[which(x$MDRO == -1)],
@ -1615,10 +1613,10 @@ mdro <- function(x = NULL,
"all"
)
if (!ab_missing(MEM) & !ab_missing(IPM) &
!ab_missing(GEN) & !ab_missing(TOB) &
!ab_missing(CIP) &
!ab_missing(CAZ) &
if (!ab_missing(MEM) && !ab_missing(IPM) &&
!ab_missing(GEN) && !ab_missing(TOB) &&
!ab_missing(CIP) &&
!ab_missing(CAZ) &&
!ab_missing(TZP)) {
x$psae <- 0
x[which(x[, MEM, drop = TRUE] == "R" | x[, IPM, drop = TRUE] == "R"), "psae"] <- 1 + x[which(x[, MEM, drop = TRUE] == "R" | x[, IPM, drop = TRUE] == "R"), "psae"]
@ -1666,7 +1664,7 @@ mdro <- function(x = NULL,
prepare_drug <- function(ab) {
# returns vector values of drug
# if `ab` is a column name, looks up the values in `x`
if (length(ab) == 1 & is.character(ab)) {
if (length(ab) == 1 && is.character(ab)) {
if (ab %in% colnames(x)) {
ab <- x[, ab, drop = TRUE]
}
@ -1727,7 +1725,7 @@ mdro <- function(x = NULL,
}
# some more info on negative results
if (verbose == TRUE) {
if (isTRUE(verbose)) {
if (guideline$code == "cmi2012") {
x[which(x$MDRO == 1 & !is.na(x$classes_affected)), "reason"] <- paste0(
x$classes_affected[which(x$MDRO == 1 & !is.na(x$classes_affected))],
@ -1742,14 +1740,14 @@ mdro <- function(x = NULL,
}
}
if (info.bak == TRUE) {
if (isTRUE(info.bak)) {
cat(group_msg)
if (sum(!is.na(x$MDRO)) == 0) {
cat(font_bold(paste0("=> Found 0 MDROs since no isolates are covered by the guideline")))
} else {
cat(font_bold(paste0(
"=> Found ", sum(x$MDRO %in% c(2:5), na.rm = TRUE), " ", guideline$type, " out of ", sum(!is.na(x$MDRO)),
" isolates (", trimws(percentage(sum(x$MDRO %in% c(2:5), na.rm = TRUE) / sum(!is.na(x$MDRO)))), ")"
"=> Found ", sum(x$MDRO %in% 2:5, na.rm = TRUE), " ", guideline$type, " out of ", sum(!is.na(x$MDRO)),
" isolates (", trimws(percentage(sum(x$MDRO %in% 2:5, na.rm = TRUE) / sum(!is.na(x$MDRO)))), ")"
)))
}
}
@ -1819,7 +1817,7 @@ mdro <- function(x = NULL,
)
}
if (verbose == TRUE) {
if (isTRUE(verbose)) {
colnames(x)[colnames(x) == col_mo] <- "microorganism"
x$microorganism <- mo_name(x$microorganism, language = NULL)
x[, c(
@ -1974,7 +1972,7 @@ run_custom_mdro_guideline <- function(df, guideline, info) {
new_mdros <- which(qry == TRUE & out == "")
if (info == TRUE) {
if (isTRUE(info)) {
cat(word_wrap(
"- Custom MDRO rule ", i, ": `", as.character(guideline[[i]]$query),
"` (", length(new_mdros), " rows matched)"
@ -1982,7 +1980,10 @@ run_custom_mdro_guideline <- function(df, guideline, info) {
}
val <- guideline[[i]]$value
out[new_mdros] <- val
reasons[new_mdros] <- paste0("matched rule ", gsub("rule", "", names(guideline)[i]), ": ", as.character(guideline[[i]]$query))
reasons[new_mdros] <- paste0(
"matched rule ",
gsub("rule", "", names(guideline)[i], fixed = TRUE), ": ", as.character(guideline[[i]]$query)
)
}
out[out == ""] <- "Negative"
reasons[out == "Negative"] <- "no rules matched"

2011
R/mo.R
View File

File diff suppressed because it is too large Load Diff

38
R/mo_matching_score.R
View File

@ -43,13 +43,17 @@
#' * \ifelse{html}{\out{<i>p<sub>n</sub></i> is the human pathogenic prevalence group of <i>n</i>, as described below;}}{p_n is the human pathogenic prevalence group of \eqn{n}, as described below;}
#' * \ifelse{html}{\out{<i>k<sub>n</sub></i> is the taxonomic kingdom of <i>n</i>, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.}}{l_n is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.}
#'
#' The grouping into human pathogenic prevalence (\eqn{p}) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. **Group 1** (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is *Enterococcus*, *Staphylococcus* or *Streptococcus*. This group consequently contains all common Gram-negative bacteria, such as *Pseudomonas* and *Legionella* and all species within the order Enterobacterales. **Group 2** consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is *Absidia*, *Acremonium*, *Actinotignum*, *Alternaria*, *Anaerosalibacter*, *Apophysomyces*, *Arachnia*, *Aspergillus*, *Aureobacterium*, *Aureobasidium*, *Bacteroides*, *Basidiobolus*, *Beauveria*, *Blastocystis*, *Branhamella*, *Calymmatobacterium*, *Candida*, *Capnocytophaga*, *Catabacter*, *Chaetomium*, *Chryseobacterium*, *Chryseomonas*, *Chrysonilia*, *Cladophialophora*, *Cladosporium*, *Conidiobolus*, *Cryptococcus*, *Curvularia*, *Exophiala*, *Exserohilum*, *Flavobacterium*, *Fonsecaea*, *Fusarium*, *Fusobacterium*, *Hendersonula*, *Hypomyces*, *Koserella*, *Lelliottia*, *Leptosphaeria*, *Leptotrichia*, *Malassezia*, *Malbranchea*, *Mortierella*, *Mucor*, *Mycocentrospora*, *Mycoplasma*, *Nectria*, *Ochroconis*, *Oidiodendron*, *Phoma*, *Piedraia*, *Pithomyces*, *Pityrosporum*, *Prevotella*, *Pseudallescheria*, *Rhizomucor*, *Rhizopus*, *Rhodotorula*, *Scolecobasidium*, *Scopulariopsis*, *Scytalidium*, *Sporobolomyces*, *Stachybotrys*, *Stomatococcus*, *Treponema*, *Trichoderma*, *Trichophyton*, *Trichosporon*, *Tritirachium* or *Ureaplasma*. **Group 3** consists of all other microorganisms.
#' The grouping into human pathogenic prevalence (\eqn{p}) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence:
#'
#' **Group 1** (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is *Enterococcus*, *Staphylococcus* or *Streptococcus*. This group consequently contains all common Gram-negative bacteria, such as *Pseudomonas* and *Legionella* and all species within the order Enterobacterales.
#'
#' **Group 2** consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is `r vector_or(MO_PREVALENT_GENERA, quotes = "*")`.
#'
#' **Group 3** consists of all other microorganisms.
#'
#' All characters in \eqn{x} and \eqn{n} are ignored that are other than A-Z, a-z, 0-9, spaces and parentheses.
#'
#' All matches are sorted descending on their matching score and for all user input values, the top match will be returned. This will lead to the effect that e.g., `"E. coli"` will return the microbial ID of *Escherichia coli* (\eqn{m = `r round(mo_matching_score("E. coli", "Escherichia coli"), 3)`}, a highly prevalent microorganism found in humans) and not *Entamoeba coli* (\eqn{m = `r round(mo_matching_score("E. coli", "Entamoeba coli"), 3)`}, a less prevalent microorganism in humans), although the latter would alphabetically come first.
#'
#' Since `AMR` version 1.8.1, common microorganism abbreviations are ignored in determining the matching score. These abbreviations are currently: `r vector_and(pkg_env$mo_field_abbreviations, quotes = FALSE)`.
#' @export
#' @inheritSection AMR Reference Data Publicly Available
#' @examples
@ -68,16 +72,6 @@ mo_matching_score <- function(x, n) {
# no dots and other non-whitespace characters
x <- gsub("[^a-zA-Z0-9 \\(\\)]+", "", x)
# remove abbreviations known to the field
x <- gsub(paste0(
"(^|[^a-z0-9]+)(",
paste0(pkg_env$mo_field_abbreviations, collapse = "|"),
")([^a-z0-9]+|$)"
),
"", x,
perl = TRUE, ignore.case = TRUE
)
# only keep one space
x <- gsub(" +", " ", x)
@ -93,12 +87,18 @@ mo_matching_score <- function(x, n) {
l_n <- nchar(n)
lev <- double(length = length(x))
l_n.lev <- double(length = length(x))
for (i in seq_len(length(x))) {
# determine Levenshtein distance, but maximise to nchar of n
lev[i] <- utils::adist(x[i], n[i], ignore.case = FALSE, fixed = TRUE, costs = c(ins = 1, del = 1, sub = 1))
# minimum of (l_n, Levenshtein distance)
l_n.lev[i] <- min(l_n[i], as.double(lev[i]))
}
lev <- unlist(Map(
f = utils::adist,
x,
n,
ignore.case = FALSE,
USE.NAMES = FALSE,
fixed = TRUE
))
l_n.lev[l_n < lev] <- l_n[l_n < lev]
l_n.lev[lev < l_n] <- lev[lev < l_n]
l_n.lev[lev == l_n] <- lev[lev == l_n]
# human pathogenic prevalence (1 to 3), see ?as.mo
p_n <- MO_lookup[match(n, MO_lookup$fullname), "prevalence", drop = TRUE]
# kingdom index (Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5)

158
R/mo_property.R
View File

@ -32,7 +32,7 @@
#' @param ... other arguments passed on to [as.mo()], such as 'allow_uncertain' and 'ignore_pattern'
#' @param ab any (vector of) text that can be coerced to a valid antibiotic code with [as.ab()]
#' @param open browse the URL using [`browseURL()`][utils::browseURL()]
#' @details All functions will return the most recently known taxonomic property according to the Catalogue of Life, except for [mo_ref()], [mo_authors()] and [mo_year()]. Please refer to this example, knowing that *Escherichia blattae* was renamed to *Shimwellia blattae* in 2010:
#' @details All functions will return the most recently known taxonomic property [as included in this package][microorganisms], except for [mo_ref()], [mo_authors()] and [mo_year()]. Please refer to this example, knowing that *Escherichia blattae* was renamed to *Shimwellia blattae* in 2010:
#' - `mo_name("Escherichia blattae")` will return `"Shimwellia blattae"` (with a message about the renaming)
#' - `mo_ref("Escherichia blattae")` will return `"Burgess et al., 1973"` (with a message about the renaming)
#' - `mo_ref("Shimwellia blattae")` will return `"Priest et al., 2010"` (without a message)
@ -51,9 +51,8 @@
#'
#' The function [mo_url()] will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species.
#'
#' SNOMED codes - [mo_snomed()] - are from the `r SNOMED_VERSION$current_source`. See *Source* and the [microorganisms] data set for more info.
#' SNOMED codes - [mo_snomed()] - are from the version of `r documentation_date(TAXONOMY_VERSION$SNOMED$accessed_date)`. See *Source* and the [microorganisms] data set for more info.
#' @inheritSection mo_matching_score Matching Score for Microorganisms
#' @inheritSection catalogue_of_life Catalogue of Life
#' @inheritSection as.mo Source
#' @rdname mo_property
#' @name mo_property
@ -175,7 +174,7 @@ mo_name <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_name")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
translate_into_language(mo_validate(x = x, property = "fullname", language = language, ...),
language = language,
@ -196,7 +195,7 @@ mo_shortname <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_shortname")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
x.mo <- as.mo(x, language = language, ...)
@ -236,7 +235,7 @@ mo_subspecies <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_subspecies")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
translate_into_language(mo_validate(x = x, property = "subspecies", language = language, ...), language = language, only_unknown = TRUE)
}
@ -249,7 +248,7 @@ mo_species <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_species")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
translate_into_language(mo_validate(x = x, property = "species", language = language, ...), language = language, only_unknown = TRUE)
}
@ -262,7 +261,7 @@ mo_genus <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_genus")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
translate_into_language(mo_validate(x = x, property = "genus", language = language, ...), language = language, only_unknown = TRUE)
}
@ -275,7 +274,7 @@ mo_family <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_family")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
translate_into_language(mo_validate(x = x, property = "family", language = language, ...), language = language, only_unknown = TRUE)
}
@ -288,7 +287,7 @@ mo_order <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_order")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
translate_into_language(mo_validate(x = x, property = "order", language = language, ...), language = language, only_unknown = TRUE)
}
@ -301,7 +300,7 @@ mo_class <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_class")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
translate_into_language(mo_validate(x = x, property = "class", language = language, ...), language = language, only_unknown = TRUE)
}
@ -314,7 +313,7 @@ mo_phylum <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_phylum")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
translate_into_language(mo_validate(x = x, property = "phylum", language = language, ...), language = language, only_unknown = TRUE)
}
@ -327,7 +326,7 @@ mo_kingdom <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_kingdom")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
translate_into_language(mo_validate(x = x, property = "kingdom", language = language, ...), language = language, only_unknown = TRUE)
}
@ -344,7 +343,7 @@ mo_type <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_type")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
x.mo <- as.mo(x, language = language, ...)
out <- mo_kingdom(x.mo, language = NULL)
@ -360,24 +359,26 @@ mo_gramstain <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_gramstain")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
x.mo <- as.mo(x, language = language, ...)
# keep_synonyms = TRUE to prevent messages - they won't change Gram stain anyway
x.mo <- as.mo(x, language = language, keep_synonyms = TRUE, ...)
metadata <- get_mo_failures_uncertainties_renamed()
x <- rep(NA_character_, length(x))
# make all bacteria Gram negative
x[mo_kingdom(x.mo) == "Bacteria"] <- "Gram-negative"
x[mo_kingdom(x.mo, language = NULL, keep_synonyms = TRUE) == "Bacteria"] <- "Gram-negative"
# overwrite these 4 phyla with Gram-positives
# Source: https://itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=956097 (Cavalier-Smith, 2002)
x[(mo_phylum(x.mo) %in% c(
x[(mo_phylum(x.mo, language = NULL, keep_synonyms = TRUE) %in% c(
"Actinobacteria",
"Chloroflexi",
"Firmicutes",
"Tenericutes"
"Tenericutes",
"Bacillota" # this one is new! It was renamed from Firmicutes by Gibbons et al., 2021
) &
# but class Negativicutes (of phylum Firmicutes) are Gram-negative!
mo_class(x.mo) != "Negativicutes")
mo_class(x.mo, language = NULL, keep_synonyms = TRUE) != "Negativicutes")
# and of course our own ID for Gram-positives
| x.mo == "B_GRAMP"] <- "Gram-positive"
@ -393,7 +394,7 @@ mo_is_gram_negative <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_is_gram_negative")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
x.mo <- as.mo(x, language = language, ...)
metadata <- get_mo_failures_uncertainties_renamed()
@ -412,7 +413,7 @@ mo_is_gram_positive <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_is_gram_positive")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
x.mo <- as.mo(x, language = language, ...)
metadata <- get_mo_failures_uncertainties_renamed()
@ -431,7 +432,7 @@ mo_is_yeast <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_is_yeast")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
x.mo <- as.mo(x, language = language, ...)
metadata <- get_mo_failures_uncertainties_renamed()
@ -456,7 +457,7 @@ mo_is_intrinsic_resistant <- function(x, ab, language = get_AMR_locale(), ...) {
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(ab, allow_NA = FALSE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
x <- as.mo(x, language = language, ...)
ab <- as.ab(ab, language = NULL, flag_multiple_results = FALSE, info = FALSE)
@ -491,7 +492,7 @@ mo_snomed <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_snomed")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
mo_validate(x = x, property = "snomed", language = language, ...)
}
@ -504,7 +505,7 @@ mo_ref <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_ref")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
mo_validate(x = x, property = "ref", language = language, ...)
}
@ -517,7 +518,7 @@ mo_authors <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_authors")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
x <- mo_validate(x = x, property = "ref", language = language, ...)
# remove last 4 digits and presumably the comma and space that preceed them
@ -533,7 +534,7 @@ mo_year <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_year")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
x <- mo_validate(x = x, property = "ref", language = language, ...)
# get last 4 digits
@ -549,7 +550,7 @@ mo_lpsn <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_rank")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
mo_validate(x = x, property = "species_id", language = language, ...)
}
@ -562,7 +563,7 @@ mo_rank <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_rank")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
mo_validate(x = x, property = "rank", language = language, ...)
}
@ -575,7 +576,7 @@ mo_taxonomy <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_taxonomy")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
x <- as.mo(x, language = language, ...)
metadata <- get_mo_failures_uncertainties_renamed()
@ -603,20 +604,22 @@ mo_synonyms <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_synonyms")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
x <- as.mo(x, language = language, ...)
x.mo <- as.mo(x, language = language, ...)
metadata <- get_mo_failures_uncertainties_renamed()
IDs <- mo_name(x = x, language = NULL)
syns <- lapply(IDs, function(newname) {
res <- sort(microorganisms.old[which(microorganisms.old$fullname_new == newname), "fullname", drop = TRUE])
if (length(res) == 0) {
syns <- lapply(x.mo, function(y) {
gbif <- AMR::microorganisms$gbif[match(y, AMR::microorganisms$mo)]
lpsn <- AMR::microorganisms$lpsn[match(y, AMR::microorganisms$mo)]
out <- AMR::microorganisms[which(AMR::microorganisms$lpsn_renamed_to %in% c(gbif, lpsn)), "fullname", drop = TRUE]
if (length(out) == 0) {
NULL
} else {
res
out
}
})
if (length(syns) > 1) {
names(syns) <- mo_name(x)
result <- syns
@ -636,7 +639,7 @@ mo_info <- function(x, language = get_AMR_locale(), ...) {
x <- find_mo_col(fn = "mo_info")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
x <- as.mo(x, language = language, ...)
metadata <- get_mo_failures_uncertainties_renamed()
@ -673,21 +676,23 @@ mo_url <- function(x, open = FALSE, language = get_AMR_locale(), ...) {
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(open, allow_class = "logical", has_length = 1)
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
stop("FIX mo_url")
x.mo <- as.mo(x = x, language = language, ... = ...)
metadata <- get_mo_failures_uncertainties_renamed()
df <- microorganisms[match(x.mo, microorganisms$mo), c("mo", "fullname", "source", "kingdom", "rank"), drop = FALSE]
df$url <- ifelse(df$source == "LPSN",
paste0(CATALOGUE_OF_LIFE$url_LPSN, "/species/", gsub(" ", "-", tolower(df$fullname), fixed = TRUE)),
paste0(CATALOGUE_OF_LIFE$url_CoL, "/data/search?type=EXACT&q=", gsub(" ", "%20", df$fullname, fixed = TRUE))
)
genera <- which(df$kingdom == "Bacteria" & df$rank == "genus")
df$url[genera] <- gsub("/species/", "/genus/", df$url[genera], fixed = TRUE)
subsp <- which(df$kingdom == "Bacteria" & df$rank %in% c("subsp.", "infraspecies"))
df$url[subsp] <- gsub("/species/", "/subspecies/", df$url[subsp], fixed = TRUE)
#
# df <- AMR::microorganisms[match(x.mo, AMR::microorganisms$mo), c("mo", "fullname", "source", "kingdom", "rank"), drop = FALSE]
# df$url <- ifelse(df$source == "LPSN",
# paste0(CATALOGUE_OF_LIFE$url_LPSN, "/species/", gsub(" ", "-", tolower(df$fullname), fixed = TRUE)),
# paste0(CATALOGUE_OF_LIFE$url_CoL, "/data/search?type=EXACT&q=", gsub(" ", "%20", df$fullname, fixed = TRUE))
# )
#
# genera <- which(df$kingdom == "Bacteria" & df$rank == "genus")
# df$url[genera] <- gsub("/species/", "/genus/", df$url[genera], fixed = TRUE)
# subsp <- which(df$kingdom == "Bacteria" & df$rank %in% c("subsp.", "infraspecies"))
# df$url[subsp] <- gsub("/species/", "/subspecies/", df$url[subsp], fixed = TRUE)
u <- df$url
names(u) <- df$fullname
@ -712,48 +717,51 @@ mo_property <- function(x, property = "fullname", language = get_AMR_locale(), .
x <- find_mo_col(fn = "mo_property")
}
meet_criteria(x, allow_NA = TRUE)
meet_criteria(property, allow_class = "character", has_length = 1, is_in = colnames(microorganisms))
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
meet_criteria(property, allow_class = "character", has_length = 1, is_in = colnames(AMR::microorganisms))
language <- validate_language(language)
translate_into_language(mo_validate(x = x, property = property, language = language, ...), language = language, only_unknown = TRUE)
}
mo_validate <- function(x, property, language, ...) {
check_dataset_integrity()
dots <- list(...)
Becker <- dots$Becker
if (is.null(Becker) | property %in% c("kingdom", "phylum", "class", "order", "family", "genus")) {
Becker <- FALSE
}
Lancefield <- dots$Lancefield
if (is.null(Lancefield) | property %in% c("kingdom", "phylum", "class", "order", "family", "genus")) {
Lancefield <- FALSE
}
has_Becker_or_Lancefield <- Becker %in% c(TRUE, "all") | Lancefield %in% c(TRUE, "all")
if (tryCatch(all(x[!is.na(x)] %in% MO_lookup$mo) & !has_Becker_or_Lancefield, error = function(e) FALSE)) {
# special case for mo_* functions where class is already <mo>
x <- MO_lookup[match(x, MO_lookup$mo), property, drop = TRUE]
} else {
# try to catch an error when inputting an invalid argument
# so the 'call.' can be set to FALSE
tryCatch(x[1L] %in% MO_lookup[1, property, drop = TRUE],
tryCatch(x[1L] %in% AMR::microorganisms[1, property, drop = TRUE],
error = function(e) stop(e$message, call. = FALSE)
)
if (!all(x[!is.na(x)] %in% MO_lookup[, property, drop = TRUE]) | has_Becker_or_Lancefield) {
x <- exec_as.mo(x, property = property, language = language, ...)
dots <- list(...)
Becker <- dots$Becker
if (is.null(Becker) || property %in% c("kingdom", "phylum", "class", "order", "family", "genus")) {
Becker <- FALSE
}
Lancefield <- dots$Lancefield
if (is.null(Lancefield) || property %in% c("kingdom", "phylum", "class", "order", "family", "genus")) {
Lancefield <- FALSE
}
keep_synonyms <- dots$keep_synonyms
has_Becker_or_Lancefield_or_synonyms <- !isFALSE(keep_synonyms) || Becker %in% c(TRUE, "all") || Lancefield %in% c(TRUE, "all")
if (all(x %in% AMR::microorganisms$mo, na.rm = TRUE) && !has_Becker_or_Lancefield_or_synonyms) {
# do nothing, just don't run the other if-else's
} else if (all(x %in% AMR::microorganisms[[property]], na.rm = TRUE) && !has_Becker_or_Lancefield_or_synonyms) {
# no need to do anything, just return it
return(x)
} else {
x <- as.mo(x, language = language, ...)
}
# get property reeaaally fast using match()
x <- microorganisms[[property]][match(x, microorganisms$mo)]
if (property == "mo") {
return(set_clean_class(x, new_class = c("mo", "character")))
} else if (property == "species_id") {
return(as.double(x))
} else if (property == "snomed") {
return(as.double(eval(parse(text = x))))
return(sort(as.character(eval(parse(text = x)))))
} else {
return(x)
# everything else is character
return(as.character(x))
}
}

16
R/mo_source.R
View File

@ -127,7 +127,7 @@ set_mo_source <- function(path, destination = getOption("AMR_mo_source", "~/mo_s
mo_source_destination <- path.expand(destination)
stop_ifnot(interactive(), "this function can only be used in interactive mode, since it must ask for the user's permission to write a file to their home folder.")
stop_ifnot(interactive(), "this function can only be used in interactive mode, since it must ask for the user's permission to write a file to their file system.")
if (is.null(path) || path %in% c(FALSE, "")) {
pkg_env$mo_source <- NULL
@ -204,14 +204,14 @@ set_mo_source <- function(path, destination = getOption("AMR_mo_source", "~/mo_s
word_wrap(paste0(
"This will write create the new file '",
mo_source_destination,
"', for which your permission is needed."
"', for which your permission is required."
)),
"\n\n",
word_wrap("Do you agree that this file will be created?")
)
showQuestion <- import_fn("showQuestion", "rstudioapi", error_on_fail = FALSE)
if (!is.null(showQuestion)) {
q_continue <- showQuestion("Create new file in home directory", txt)
q_continue <- showQuestion("Create new file", txt)
} else {
q_continue <- utils::menu(choices = c("OK", "Cancel"), graphics = FALSE, title = txt)
}
@ -257,8 +257,6 @@ get_mo_source <- function(destination = getOption("AMR_mo_source", "~/mo_source.
}
check_validity_mo_source <- function(x, refer_to_name = "`reference_df`", stop_on_error = TRUE) {
check_dataset_integrity()
if (paste(deparse(substitute(x)), collapse = "") == "get_mo_source()") {
return(TRUE)
}
@ -286,9 +284,9 @@ check_validity_mo_source <- function(x, refer_to_name = "`reference_df`", stop_o
return(FALSE)
}
}
if (!all(x$mo %in% c("", microorganisms$mo, microorganisms$fullname), na.rm = TRUE)) {
if (!all(x$mo %in% c("", AMR::microorganisms$mo, AMR::microorganisms$fullname), na.rm = TRUE)) {
if (stop_on_error == TRUE) {
invalid <- x[which(!x$mo %in% c("", microorganisms$mo, microorganisms$fullname)), , drop = FALSE]
invalid <- x[which(!x$mo %in% c("", AMR::microorganisms$mo, AMR::microorganisms$fullname)), , drop = FALSE]
if (nrow(invalid) > 1) {
plural <- "s"
} else {
@ -303,14 +301,14 @@ check_validity_mo_source <- function(x, refer_to_name = "`reference_df`", stop_o
return(FALSE)
}
}
if (colnames(x)[1] != "mo" & nrow(x) > length(unique(x[, 1, drop = TRUE]))) {
if (colnames(x)[1] != "mo" && nrow(x) > length(unique(x[, 1, drop = TRUE]))) {
if (stop_on_error == TRUE) {
stop_(refer_to_name, " contains duplicate values in column '", colnames(x)[1], "'", call = FALSE)
} else {
return(FALSE)
}
}
if (colnames(x)[2] != "mo" & nrow(x) > length(unique(x[, 2, drop = TRUE]))) {
if (colnames(x)[2] != "mo" && nrow(x) > length(unique(x[, 2, drop = TRUE]))) {
if (stop_on_error == TRUE) {
stop_(refer_to_name, " contains duplicate values in column '", colnames(x)[2], "'", call = FALSE)
} else {

2
R/pca.R
View File

@ -97,7 +97,7 @@ pca <- function(x,
error = function(e) stop(e$message, call. = FALSE)
)
if (length(new_list[[i]]) == 1) {
if (is.character(new_list[[i]]) & new_list[[i]] %in% colnames(x)) {
if (is.character(new_list[[i]]) && new_list[[i]] %in% colnames(x)) {
# this is to support quoted variables: df %pm>% pca("mycol1", "mycol2")
new_list[[i]] <- x[, new_list[[i]]]
} else {

14
R/plot.R
View File

@ -97,7 +97,7 @@ plot.mic <- function(x,
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(xlab, allow_class = "character", has_length = 1)
meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(expand, allow_class = "logical", has_length = 1)
# translate if not specifically set
@ -188,7 +188,7 @@ barplot.mic <- function(height,
meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
meet_criteria(guideline, allow_class = "character", has_length = 1)
meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(expand, allow_class = "logical", has_length = 1)
# translate if not specifically set
@ -236,7 +236,7 @@ autoplot.mic <- function(object,
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(xlab, allow_class = "character", has_length = 1)
meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(expand, allow_class = "logical", has_length = 1)
# translate if not specifically set
@ -336,7 +336,7 @@ plot.disk <- function(x,
meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
meet_criteria(guideline, allow_class = "character", has_length = 1)
meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(expand, allow_class = "logical", has_length = 1)
# translate if not specifically set
@ -427,7 +427,7 @@ barplot.disk <- function(height,
meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
meet_criteria(guideline, allow_class = "character", has_length = 1)
meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(expand, allow_class = "logical", has_length = 1)
# translate if not specifically set
@ -475,7 +475,7 @@ autoplot.disk <- function(object,
meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
meet_criteria(guideline, allow_class = "character", has_length = 1)
meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(expand, allow_class = "logical", has_length = 1)
# translate if not specifically set
@ -639,7 +639,7 @@ barplot.rsi <- function(height,
meet_criteria(main, allow_class = "character", has_length = 1, allow_NULL = TRUE)
meet_criteria(ylab, allow_class = "character", has_length = 1)
meet_criteria(colours_RSI, allow_class = "character", has_length = c(1, 3))
meet_criteria(language, has_length = 1, is_in = c(LANGUAGES_SUPPORTED, ""), allow_NULL = TRUE, allow_NA = TRUE)
language <- validate_language(language)
meet_criteria(expand, allow_class = "logical", has_length = 1)
# translate if not specifically set

1
R/random.R
View File

@ -87,7 +87,6 @@ random_rsi <- function(size = NULL, prob_RSI = c(0.33, 0.33, 0.33), ...) {
}
random_exec <- function(type, size, mo = NULL, ab = NULL) {
check_dataset_integrity()
df <- rsi_translation %pm>%
pm_filter(guideline %like% "EUCAST") %pm>%
pm_arrange(pm_desc(guideline)) %pm>%

22
R/resistance_predict.R
View File

@ -132,18 +132,6 @@ resistance_predict <- function(x,
x.bak <- x
x <- as.data.frame(x, stringsAsFactors = FALSE)
dots <- unlist(list(...))
if (length(dots) != 0) {
# backwards compatibility with old arguments
dots.names <- names(dots)
if ("tbl" %in% dots.names) {
x <- dots[which(dots.names == "tbl")]
}
if ("I_as_R" %in% dots.names) {
warning_("in `resistance_predict()`: I_as_R is deprecated - use I_as_S instead.")
}
}
# -- date
if (is.null(col_date)) {
col_date <- search_type_in_df(x = x, type = "date")
@ -167,10 +155,10 @@ resistance_predict <- function(x,
df[, col_ab] <- droplevels(as.rsi(df[, col_ab, drop = TRUE]))
if (I_as_S == TRUE) {
# then I as S
df[, col_ab] <- gsub("I", "S", df[, col_ab, drop = TRUE])
df[, col_ab] <- gsub("I", "S", df[, col_ab, drop = TRUE], fixed = TRUE)
} else {
# then I as R
df[, col_ab] <- gsub("I", "R", df[, col_ab, drop = TRUE])
df[, col_ab] <- gsub("I", "R", df[, col_ab, drop = TRUE], fixed = TRUE)
}
df[, col_ab] <- ifelse(is.na(df[, col_ab, drop = TRUE]), 0, df[, col_ab, drop = TRUE])
@ -257,10 +245,10 @@ resistance_predict <- function(x,
df_prediction$se_max <- as.integer(df_prediction$se_max)
} else {
# se_max not above 1
df_prediction$se_max <- ifelse(df_prediction$se_max > 1, 1, df_prediction$se_max)
df_prediction$se_max <- pmin(df_prediction$se_max, 1)
}
# se_min not below 0
df_prediction$se_min <- ifelse(df_prediction$se_min < 0, 0, df_prediction$se_min)
df_prediction$se_min <- pmax(df_prediction$se_min, 0)
df_observations <- data.frame(
year = df$year,
@ -279,7 +267,7 @@ resistance_predict <- function(x,
df_prediction$se_max <- ifelse(!is.na(df_prediction$observed), NA, df_prediction$se_max)
}
df_prediction$value <- ifelse(df_prediction$value > 1, 1, ifelse(df_prediction$value < 0, 0, df_prediction$value))
df_prediction$value <- ifelse(df_prediction$value > 1, 1, pmax(df_prediction$value, 0))
df_prediction <- df_prediction[order(df_prediction$year), , drop = FALSE]
out <- as_original_data_class(df_prediction, class(x.bak))

6
R/rsi.R
View File

@ -986,9 +986,9 @@ pillar_shaft.rsi <- function(x, ...) {
# colours will anyway not work when has_colour() == FALSE,
# but then the indentation should also not be applied
out[is.na(x)] <- font_grey(" NA")
out[x == "R"] <- font_rsi_R_bg(font_black(" R "))
out[x == "S"] <- font_rsi_S_bg(font_black(" S "))
out[x == "I"] <- font_rsi_I_bg(font_black(" I "))
out[x == "R"] <- font_red_bg(" R ")
out[x == "S"] <- font_green_bg(" S ")
out[x == "I"] <- font_orange_bg(" I ")
}
create_pillar_column(out, align = "left", width = 5)
}

12
R/rsi_calc.R
View File

@ -72,7 +72,7 @@ rsi_calc <- function(...,
} else {
dots <- dots[2:length(dots)]
}
if (length(dots) == 0 | all(dots == "df")) {
if (length(dots) == 0 || all(dots == "df")) {
# for complete data.frames, like example_isolates %pm>% select(AMC, GEN) %pm>% proportion_S()
# and the old rsi function, which has "df" as name of the first argument
x <- dots_df
@ -137,12 +137,12 @@ rsi_calc <- function(...,
FUN = min
)
numerator <- sum(as.integer(y) %in% as.integer(ab_result), na.rm = TRUE)
denominator <- sum(vapply(FUN.VALUE = logical(1), x_transposed, function(y) !(any(is.na(y)))))
denominator <- sum(vapply(FUN.VALUE = logical(1), x_transposed, function(y) !(anyNA(y))))
} else {
# may contain NAs in any column
other_values <- setdiff(c(NA, levels(ab_result)), ab_result)
numerator <- sum(vapply(FUN.VALUE = logical(1), x_transposed, function(y) any(y %in% ab_result, na.rm = TRUE)))
denominator <- sum(vapply(FUN.VALUE = logical(1), x_transposed, function(y) !(all(y %in% other_values) & any(is.na(y)))))
denominator <- sum(vapply(FUN.VALUE = logical(1), x_transposed, function(y) !(all(y %in% other_values) & anyNA(y))))
}
} else {
# x is not a data.frame
@ -228,9 +228,7 @@ rsi_calc_df <- function(type, # "proportion", "count" or "both"
meet_criteria(combine_SI, allow_class = "logical", has_length = 1, .call_depth = 1)
meet_criteria(combine_SI_missing, allow_class = "logical", has_length = 1, .call_depth = 1)
check_dataset_integrity()
if (isTRUE(combine_IR) & isTRUE(combine_SI_missing)) {
if (isTRUE(combine_IR) && isTRUE(combine_SI_missing)) {
combine_SI <- FALSE
}
stop_if(isTRUE(combine_SI) & isTRUE(combine_IR), "either `combine_SI` or `combine_IR` can be TRUE, not both", call = -2)
@ -249,7 +247,7 @@ rsi_calc_df <- function(type, # "proportion", "count" or "both"
}
data <- as.data.frame(data, stringsAsFactors = FALSE)
if (isTRUE(combine_SI) | isTRUE(combine_IR)) {
if (isTRUE(combine_SI) || isTRUE(combine_IR)) {
for (i in seq_len(ncol(data))) {
if (is.rsi(data[, i, drop = TRUE])) {
data[, i] <- as.character(data[, i, drop = TRUE])

BIN
R/sysdata.rda
View File

Binary file not shown.

63
R/translate.R
View File

@ -30,14 +30,28 @@
#' @param language language to choose. Use one of these supported language names or ISO-639-1 codes: `r paste0('"', sapply(LANGUAGES_SUPPORTED_NAMES, function(x) x[[1]]), '" ("' , LANGUAGES_SUPPORTED, '")', collapse = ", ")`.
#' @details The currently `r length(LANGUAGES_SUPPORTED)` supported languages are `r vector_and(sapply(LANGUAGES_SUPPORTED_NAMES, function(x) x[[1]]), quotes = FALSE, sort = FALSE)`. All these languages have translations available for all antimicrobial agents and colloquial microorganism names.
#'
#' **To silence language notes when this package loads** on a non-English operating system, please set the option `AMR_locale` in your `.Rprofile` file like this:
#'
#' ```r
#' # Open .Rprofile file
#' utils::file.edit("~/.Rprofile")
#'
#' # Add e.g. Italian support to that file using:
#' options(AMR_locale = "Italian")
#' # or using:
#' AMR::set_AMR_locale("Italian")
#'
#' # And save the file!
#' ```
#'
#' Please read about adding or updating a language in [our Wiki](https://github.com/msberends/AMR/wiki/).
#'
#' ## Changing the Default Language
#' The system language will be used at default (as returned by `Sys.getenv("LANG")` or, if `LANG` is not set, [`Sys.getlocale("LC_COLLATE")`][Sys.getlocale()]), if that language is supported. But the language to be used can be overwritten in two ways and will be checked in this order:
#'
#' 1. Setting the R option `AMR_locale`, either by using `set_AMR_locale()` or by running e.g. `options(AMR_locale = "de")`.
#' 1. Setting the R option `AMR_locale`, either by using e.g. `set_AMR_locale("German")` or by running e.g. `options(AMR_locale = "German")`.
#'
#' Note that setting an \R option only works in the same session. Save the command `options(AMR_locale = "(your language)")` to your `.Rprofile` file to apply it for every session.
#' Note that setting an \R option only works in the same session. Save the command `options(AMR_locale = "(your language)")` to your `.Rprofile` file to apply it for every session. Run `utils::file.edit("~/.Rprofile")` to edit your `.Rprofile` file.
#' 2. Setting the system variable `LANGUAGE` or `LANG`, e.g. by adding `LANGUAGE="de_DE.utf8"` to your `.Renviron` file in your home directory.
#'
#' Thus, if the R option `AMR_locale` is set, the system variables `LANGUAGE` and `LANG` will be ignored.
@ -47,16 +61,22 @@
#' @examples
#' # Current settings (based on system language)
#' ab_name("Ciprofloxacin")
#' mo_name("Coagulase-negative Staphylococcus")
#' mo_name("Coagulase-negative Staphylococcus (CoNS)")
#'
#' # setting another language
#' set_AMR_locale("Greek")
#' ab_name("Ciprofloxacin")
#' mo_name("Coagulase-negative Staphylococcus")
#'
#' set_AMR_locale("Spanish")
#' ab_name("Ciprofloxacin")
#' mo_name("Coagulase-negative Staphylococcus")
#' mo_name("Coagulase-negative Staphylococcus (CoNS)")
#'
#' # setting yet another language
#' set_AMR_locale("Greek")
#' ab_name("Ciprofloxacin")
#' mo_name("Coagulase-negative Staphylococcus (CoNS)")
#'
#' # setting yet another language
#' set_AMR_locale("Ukrainian")
#' ab_name("Ciprofloxacin")
#' mo_name("Coagulase-negative Staphylococcus (CoNS)")
#'
#' # set_AMR_locale() understands endonyms, English exonyms, and ISO-639-1:
#' set_AMR_locale("Deutsch")
@ -87,7 +107,7 @@ get_AMR_locale <- function() {
message_(
"Assuming the ", LANGUAGES_SUPPORTED_NAMES[[lang]]$exonym, " language (",
LANGUAGES_SUPPORTED_NAMES[[lang]]$endonym, ") for the AMR package. Change this with `set_AMR_locale()`. ",
"This note will be shown once per session."
"This note will be shown once per session but can be silenced, see `?set_AMR_locale()`."
)
}
lang
@ -98,13 +118,21 @@ get_AMR_locale <- function() {
set_AMR_locale <- function(language) {
language <- validate_language(language)
options(AMR_locale = language)
if (interactive() || identical(Sys.getenv("IN_PKGDOWN"), "true")) {
# show which language to use now
message_("Using the ", LANGUAGES_SUPPORTED_NAMES[[language]]$exonym, " language (", LANGUAGES_SUPPORTED_NAMES[[language]]$endonym, ") for the AMR package for this session.")
}
}
#' @rdname translate
#' @export
reset_AMR_locale <- function() {
options(AMR_locale = NULL)
if (interactive() || identical(Sys.getenv("IN_PKGDOWN"), "true")) {
# show which language to use now
language <- suppressMessages(get_AMR_locale())
message_("Using the ", LANGUAGES_SUPPORTED_NAMES[[language]]$exonym, " language (", LANGUAGES_SUPPORTED_NAMES[[language]]$endonym, ") for the AMR package for this session.")
}
}
#' @rdname translate
@ -115,10 +143,10 @@ translate_AMR <- function(x, language = get_AMR_locale()) {
validate_language <- function(language, extra_txt = character(0)) {
if (trimws(tolower(language)) %in% c("en", "english", "", "false", NA)) {
if (isTRUE(trimws(tolower(language[1])) %in% c("en", "english", "", "false", NA)) || length(language) == 0) {
return("en")
}
lang <- find_language(language, fallback = FALSE)
lang <- find_language(language[1], fallback = FALSE)
stop_ifnot(length(lang) > 0 && lang %in% LANGUAGES_SUPPORTED,
"unsupported language for AMR package", extra_txt, ": \"", language, "\". Use one of these language names or ISO-639-1 codes: ",
paste0('"', vapply(FUN.VALUE = character(1), LANGUAGES_SUPPORTED_NAMES, function(x) x[[1]]),
@ -131,19 +159,19 @@ validate_language <- function(language, extra_txt = character(0)) {
}
find_language <- function(language, fallback = TRUE) {
language <- Map(function(l, n, check = language) {
language <- Map(LANGUAGES_SUPPORTED_NAMES,
LANGUAGES_SUPPORTED,
f = function(l, n, check = language) {
grepl(paste0(
"^(", l[1], "|", l[2], "|",
n, "(_|$)|", toupper(n), "(_|$))"
),
check,
ignore.case = FALSE,
ignore.case = TRUE,
perl = TRUE,
useBytes = FALSE
)
},
LANGUAGES_SUPPORTED_NAMES,
LANGUAGES_SUPPORTED,
USE.NAMES = TRUE
)
language <- names(which(language == TRUE))
@ -160,10 +188,7 @@ translate_into_language <- function(from,
only_unknown = FALSE,
only_affect_ab_names = FALSE,
only_affect_mo_names = FALSE) {
if (is.null(language)) {
return(from)
}
if (language %in% c("en", "", NA)) {
if (is.null(language) || language[1] %in% c("en", "", NA)) {
return(from)
}

61
R/zzz.R
View File

@ -26,12 +26,18 @@
# set up package environment, used by numerous AMR functions
pkg_env <- new.env(hash = FALSE)
pkg_env$mo_failed <- character(0)
pkg_env$mo_field_abbreviations <- c(
"AIEC", "ATEC", "BORSA", "CRSM", "DAEC", "EAEC",
"EHEC", "EIEC", "EPEC", "ETEC", "GISA", "MRPA",
"MRSA", "MRSE", "MSSA", "MSSE", "NMEC", "PISP",
"PRSP", "STEC", "UPEC", "VISA", "VISP", "VRE",
"VRSA", "VRSP"
pkg_env$mo_uncertainties <- data.frame(
uncertainty = integer(0),
input = character(0),
fullname = character(0),
mo = character(0),
candidates = character(0),
stringsAsFactors = FALSE
)
pkg_env$mo_previously_coerced <- data.frame(
x = character(0),
mo = character(0),
stringsAsFactors = FALSE
)
pkg_env$rsi_interpretation_history <- data.frame(
datetime = Sys.time()[0],
@ -62,7 +68,7 @@ if (utf8_supported && !is_latex) {
pkg_env$info_icon <- "i"
}
.onLoad <- function(...) {
.onLoad <- function(lib, pkg) {
# Support for tibble headers (type_sum) and tibble columns content (pillar_shaft)
# without the need to depend on other packages. This was suggested by the
# developers of the vctrs package:
@ -135,7 +141,6 @@ if (utf8_supported && !is_latex) {
# they cannot be part of R/sysdata.rda since CRAN thinks it would make the package too large (+3 MB)
assign(x = "AB_lookup", value = create_AB_lookup(), envir = asNamespace("AMR"))
assign(x = "MO_lookup", value = create_MO_lookup(), envir = asNamespace("AMR"))
assign(x = "MO.old_lookup", value = create_MO.old_lookup(), envir = asNamespace("AMR"))
# for mo_is_intrinsic_resistant() - saves a lot of time when executed on this vector
assign(x = "INTRINSIC_R", value = create_intr_resistance(), envir = asNamespace("AMR"))
}
@ -157,30 +162,34 @@ create_MO_lookup <- function() {
# all the rest
MO_lookup[which(is.na(MO_lookup$kingdom_index)), "kingdom_index"] <- 5
# use this paste instead of `fullname` to work with Viridans Group Streptococci, etc.
if (length(MO_FULLNAME_LOWER) == nrow(MO_lookup)) {
MO_lookup$fullname_lower <- MO_FULLNAME_LOWER
} else {
MO_lookup$fullname_lower <- ""
warning("MO table updated - Run: source(\"data-raw/_pre_commit_hook.R\")", call. = FALSE)
}
# # use this paste instead of `fullname` to work with Viridans Group Streptococci, etc.
# if (length(MO_FULLNAME_LOWER) == nrow(MO_lookup)) {
# MO_lookup$fullname_lower <- MO_FULLNAME_LOWER
# } else {
# MO_lookup$fullname_lower <- ""
# warning("MO table updated - Run: source(\"data-raw/_pre_commit_hook.R\")", call. = FALSE)
# }
# add a column with only "e coli" like combinations
MO_lookup$g_species <- gsub("^([a-z])[a-z]+ ([a-z]+) ?.*", "\\1 \\2", MO_lookup$fullname_lower, perl = TRUE)
MO_lookup$fullname_lower <- create_MO_fullname_lower()
MO_lookup$full_first <- substr(MO_lookup$fullname_lower, 1, 1)
MO_lookup$species_first <- substr(MO_lookup$species, 1, 1)
# so arrange data on prevalence first, then kingdom, then full name
MO_lookup[order(MO_lookup$prevalence, MO_lookup$kingdom_index, MO_lookup$fullname_lower), , drop = FALSE]
}
create_MO.old_lookup <- function() {
MO.old_lookup <- AMR::microorganisms.old
MO.old_lookup$fullname_lower <- trimws(gsub("[^.a-z0-9/ \\-]+", "", tolower(trimws(MO.old_lookup$fullname))))
# add a column with only "e coli"-like combinations
MO.old_lookup$g_species <- trimws(gsub("^([a-z])[a-z]+ ([a-z]+) ?.*", "\\1 \\2", MO.old_lookup$fullname_lower))
# so arrange data on prevalence first, then full name
MO.old_lookup[order(MO.old_lookup$prevalence, MO.old_lookup$fullname_lower), , drop = FALSE]
create_MO_fullname_lower <- function() {
MO_lookup <- AMR::microorganisms
# use this paste instead of `fullname` to work with Viridans Group Streptococci, etc.
MO_lookup$fullname_lower <- tolower(trimws(paste(
MO_lookup$genus,
MO_lookup$species,
MO_lookup$subspecies
)))
ind <- MO_lookup$genus == "" | grepl("^[(]unknown ", MO_lookup$fullname, perl = TRUE)
MO_lookup[ind, "fullname_lower"] <- tolower(MO_lookup[ind, "fullname", drop = TRUE])
MO_lookup$fullname_lower <- trimws(gsub("[^.a-z0-9/ \\-]+", "", MO_lookup$fullname_lower, perl = TRUE))
MO_lookup$fullname_lower
}
create_intr_resistance <- function() {

2
README.md
View File

@ -10,7 +10,7 @@
`AMR` is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. It is currently being used in over 175 countries.
After installing this package, R knows ~71,000 distinct microbial species and all ~570 antibiotic, antimycotic, and antiviral drugs by name and code (including ATC, WHONET/EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data. Antimicrobial names and group names are available in Danish, Dutch, English, French, German, Italian, Portuguese and Spanish.
After installing this package, R knows ~63,000 distinct microbial species and all ~570 antibiotic, antimycotic, and antiviral drugs by name and code (including ATC, WHONET/EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data. Antimicrobial names and group names are available in Danish, Dutch, English, French, German, Italian, Portuguese and Spanish.
This package is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice Foundation and University Medical Center Groningen. This R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.

3
_pkgdown.yml
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@ -41,7 +41,6 @@ template:
opengraph:
twitter:
creator: "@msberends"
site: "@univgroningen"
card: summary_large_image
news:
@ -54,7 +53,7 @@ footer:
right: [logo]
components:
devtext: '<code>AMR</code> (for R). Developed at the <a target="_blank" href="https://www.rug.nl">University of Groningen</a> in collaboration with non-profit organisations<br><a target="_blank" href="https://www.certe.nl">Certe Medical Diagnostics and Advice Foundation</a> and <a target="_blank" href="https://www.umcg.nl">University Medical Center Groningen</a>.'
logo: '<a target="_blank" href="https://www.rug.nl"><img src="https://github.com/msberends/AMR/raw/main/pkgdown/logos/logo_rug.svg" style="max-width: 200px;"></a>'
logo: '<a target="_blank" href="https://www.rug.nl"><img src="https://github.com/msberends/AMR/raw/main/pkgdown/logos/logo_rug.svg" style="max-width: 150px;"></a>'
home:
sidebar:

67
data-raw/_pre_commit_hook.R
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@ -113,8 +113,10 @@ create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
"ureilyticus",
"vitulinus", "vitulus", "warneri", "xylosus",
"caledonicus", "canis",
"durrellii", "lloydii"
"durrellii", "lloydii",
"ratti", "taiwanensis"
) |
# old, now renamed to S. schleiferi (but still as synonym in our data of course):
(MO_staph$species == "schleiferi" & MO_staph$subspecies %in% c("schleiferi", ""))),
"mo",
drop = TRUE
@ -128,8 +130,10 @@ create_species_cons_cops <- function(type = c("CoNS", "CoPS")) {
"hyicus", "intermedius",
"pseudintermedius", "pseudointermedius",
"schweitzeri", "simiae",
"roterodami"
"roterodami",
"singaporensis"
) |
# old, now renamed to S. coagulans (but still as synonym in our data of course):
(MO_staph$species == "schleiferi" & MO_staph$subspecies == "coagulans")),
"mo",
drop = TRUE
@ -151,22 +155,26 @@ create_MO_fullname_lower <- function() {
}
MO_CONS <- create_species_cons_cops("CoNS")
MO_COPS <- create_species_cons_cops("CoPS")
MO_STREP_ABCG <- as.mo(MO_lookup[which(MO_lookup$genus == "Streptococcus"), "mo", drop = TRUE], Lancefield = TRUE) %in% c("B_STRPT_GRPA", "B_STRPT_GRPB", "B_STRPT_GRPC", "B_STRPT_GRPG")
MO_STREP_ABCG <- MO_lookup$mo[which(MO_lookup$genus == "Streptococcus" &
MO_lookup$species %in% c(
"pyogenes", "agalactiae", "dysgalactiae", "equi", "anginosus", "sanguinis", "salivarius",
"group A", "group B", "group C", "group D", "group F", "group G", "group H", "group K", "group L"
))]
MO_FULLNAME_LOWER <- create_MO_fullname_lower()
MO_PREVALENT_GENERA <- c(
"Absidia", "Acholeplasma", "Acremonium", "Actinotignum", "Aedes", "Alistipes", "Alloprevotella",
"Alternaria", "Anaerosalibacter", "Ancylostoma", "Angiostrongylus", "Anisakis", "Anopheles",
"Absidia", "Acanthamoeba", "Acholeplasma", "Acremonium", "Actinotignum", "Aedes", "Alistipes", "Alloprevotella",
"Alternaria", "Amoeba", "Anaerosalibacter", "Ancylostoma", "Angiostrongylus", "Anisakis", "Anopheles",
"Apophysomyces", "Arachnia", "Aspergillus", "Aureobasidium", "Bacteroides", "Basidiobolus",
"Beauveria", "Bergeyella", "Blastocystis", "Blastomyces", "Borrelia", "Brachyspira", "Branhamella",
"Butyricimonas", "Candida", "Capillaria", "Capnocytophaga", "Catabacter", "Cetobacterium", "Chaetomium",
"Chlamydia", "Chlamydophila", "Chryseobacterium", "Chrysonilia", "Cladophialophora", "Cladosporium",
"Conidiobolus", "Contracaecum", "Cordylobia", "Cryptococcus", "Curvularia", "Deinococcus", "Demodex",
"Dermatobia", "Diphyllobothrium", "Dirofilaria", "Dysgonomonas", "Echinostoma", "Elizabethkingia",
"Empedobacter", "Enterobius", "Exophiala", "Exserohilum", "Fasciola", "Flavobacterium", "Fonsecaea",
"Dermatobia", "Dientamoeba", "Diphyllobothrium", "Dirofilaria", "Dysgonomonas", "Echinostoma", "Elizabethkingia",
"Empedobacter", "Entamoeba", "Enterobius", "Exophiala", "Exserohilum", "Fasciola", "Flavobacterium", "Fonsecaea",
"Fusarium", "Fusobacterium", "Giardia", "Haloarcula", "Halobacterium", "Halococcus", "Hendersonula",
"Heterophyes", "Histoplasma", "Hymenolepis", "Hypomyces", "Hysterothylacium", "Lelliottia",
"Heterophyes", "Histomonas", "Histoplasma", "Hymenolepis", "Hypomyces", "Hysterothylacium", "Leishmania", "Lelliottia",
"Leptosphaeria", "Leptotrichia", "Lucilia", "Lumbricus", "Malassezia", "Malbranchea", "Metagonimus",
"Microsporum", "Mortierella", "Mucor", "Mycocentrospora", "Mycoplasma", "Myroides", "Necator",
"Microsporidium", "Microsporum", "Mortierella", "Mucor", "Mycocentrospora", "Mycoplasma", "Myroides", "Necator",
"Nectria", "Ochroconis", "Odoribacter", "Oesophagostomum", "Oidiodendron", "Opisthorchis",
"Ornithobacterium", "Parabacteroides", "Pediculus", "Pedobacter", "Phlebotomus", "Phocaeicola",
"Phocanema", "Phoma", "Piedraia", "Pithomyces", "Pityrosporum", "Porphyromonas", "Prevotella",
@ -175,7 +183,7 @@ MO_PREVALENT_GENERA <- c(
"Spirometra", "Spiroplasma", "Sporobolomyces", "Stachybotrys", "Streptobacillus", "Strongyloides",
"Syngamus", "Taenia", "Tannerella", "Tenacibaculum", "Terrimonas", "Toxocara", "Treponema", "Trichinella",
"Trichobilharzia", "Trichoderma", "Trichomonas", "Trichophyton", "Trichosporon", "Trichostrongylus",
"Trichuris", "Tritirachium", "Trombicula", "Tunga", "Ureaplasma", "Victivallis", "Wautersiella",
"Trichuris", "Tritirachium", "Trypanosoma", "Trombicula", "Tunga", "Ureaplasma", "Victivallis", "Wautersiella",
"Weeksella", "Wuchereria"
)
@ -281,7 +289,7 @@ create_AB_lookup <- function() {
AB_LOOKUP <- create_AB_lookup()
# Export to package as internal data ----
usethis::ui_info(paste0("Saving {usethis::ui_value('sysdata.rda')} to {usethis::ui_value('R/')}"))
usethis::ui_info(paste0("Updating internal package data"))
suppressMessages(usethis::use_data(EUCAST_RULES_DF,
TRANSLATIONS,
LANGUAGES_SUPPORTED_NAMES,
@ -360,7 +368,7 @@ changed_md5 <- function(object) {
# give official names to ABs and MOs
rsi <- rsi_translation %>%
mutate(mo_name = mo_name(mo, language = NULL), .after = mo) %>%
mutate(mo_name = mo_name(mo, language = NULL, keep_synonyms = TRUE, info = FALSE), .after = mo) %>%
mutate(ab_name = ab_name(ab, language = NULL), .after = ab)
if (changed_md5(rsi)) {
usethis::ui_info(paste0("Saving {usethis::ui_value('rsi_translation')} to {usethis::ui_value('data-raw/')}"))
@ -392,19 +400,6 @@ if (changed_md5(microorganisms)) {
try(arrow::write_parquet(microorganisms, "data-raw/microorganisms.parquet"), silent = TRUE)
}
if (changed_md5(microorganisms.old)) {
usethis::ui_info(paste0("Saving {usethis::ui_value('microorganisms.old')} to {usethis::ui_value('data-raw/')}"))
write_md5(microorganisms.old)
try(saveRDS(microorganisms.old, "data-raw/microorganisms.old.rds", version = 2, compress = "xz"), silent = TRUE)
try(write.table(microorganisms.old, "data-raw/microorganisms.old.txt", sep = "\t", na = "", row.names = FALSE), silent = TRUE)
try(haven::write_sas(microorganisms.old, "data-raw/microorganisms.old.sas"), silent = TRUE)
try(haven::write_sav(microorganisms.old, "data-raw/microorganisms.old.sav"), silent = TRUE)
try(haven::write_dta(microorganisms.old, "data-raw/microorganisms.old.dta"), silent = TRUE)
try(openxlsx::write.xlsx(microorganisms.old, "data-raw/microorganisms.old.xlsx"), silent = TRUE)
try(arrow::write_feather(microorganisms.old, "data-raw/microorganisms.old.feather"), silent = TRUE)
try(arrow::write_parquet(microorganisms.old, "data-raw/microorganisms.old.parquet"), silent = TRUE)
}
ab <- dplyr::mutate_if(antibiotics, ~ !is.numeric(.), as.character)
if (changed_md5(ab)) {
usethis::ui_info(paste0("Saving {usethis::ui_value('antibiotics')} to {usethis::ui_value('data-raw/')}"))
@ -435,7 +430,7 @@ if (changed_md5(av)) {
# give official names to ABs and MOs
intrinsicR <- data.frame(
microorganism = mo_name(intrinsic_resistant$mo, language = NULL),
microorganism = mo_name(intrinsic_resistant$mo, language = NULL, keep_synonyms = TRUE, info = FALSE),
antibiotic = ab_name(intrinsic_resistant$ab, language = NULL),
stringsAsFactors = FALSE
)
@ -481,21 +476,21 @@ invisible(capture.output(urlchecker::url_update()))
# Document pkg ------------------------------------------------------------
usethis::ui_info("Documenting package")
suppressMessages(devtools::document(quiet = TRUE))
if (interactive()) {
usethis::ui_info("Documenting package")
suppressMessages(devtools::document(quiet = TRUE))
}
# Style pkg ---------------------------------------------------------------
usethis::ui_info("Styling package")
invisible(capture.output(styler::style_pkg(
if (interactive()) {
# only when sourcing this file ourselves
usethis::ui_info("Styling package")
styler::style_pkg(
style = styler::tidyverse_style,
filetype = c("R", "Rmd")
)))
invisible(capture.output(styler::style_dir(
path = "inst", # unit tests
style = styler::tidyverse_style,
filetype = c("R", "Rmd")
)))
)
}
# Finished ----------------------------------------------------------------

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data-raw/intrinsic_resistant.txt
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@ -7617,11 +7617,11 @@
"Actinoalloteichus spitiensis" "Nalidixic acid"
"Actinoalloteichus spitiensis" "Polymyxin B"
"Actinoalloteichus spitiensis" "Temocillin"
"Actinobacteria" "Aztreonam"
"Actinobacteria" "Colistin"
"Actinobacteria" "Nalidixic acid"
"Actinobacteria" "Polymyxin B"
"Actinobacteria" "Temocillin"
"Actinobacteriota" "Aztreonam"
"Actinobacteriota" "Colistin"
"Actinobacteriota" "Nalidixic acid"
"Actinobacteriota" "Polymyxin B"
"Actinobacteriota" "Temocillin"
"Actinobaculum" "Aztreonam"
"Actinobaculum" "Colistin"
"Actinobaculum" "Nalidixic acid"
@ -8877,11 +8877,6 @@
"Actinopolysporaceae" "Nalidixic acid"
"Actinopolysporaceae" "Polymyxin B"
"Actinopolysporaceae" "Temocillin"
"Actinopolysporales" "Aztreonam"
"Actinopolysporales" "Colistin"
"Actinopolysporales" "Nalidixic acid"
"Actinopolysporales" "Polymyxin B"
"Actinopolysporales" "Temocillin"
"Actinorectispora" "Aztreonam"
"Actinorectispora" "Colistin"
"Actinorectispora" "Nalidixic acid"
@ -9272,16 +9267,6 @@
"Aestuariimicrobium" "Nalidixic acid"
"Aestuariimicrobium" "Polymyxin B"
"Aestuariimicrobium" "Temocillin"
"Aestuariimicrobium kwangyangense" "Aztreonam"
"Aestuariimicrobium kwangyangense" "Colistin"
"Aestuariimicrobium kwangyangense" "Nalidixic acid"
"Aestuariimicrobium kwangyangense" "Polymyxin B"
"Aestuariimicrobium kwangyangense" "Temocillin"
"Aestuariimicrobium soli" "Aztreonam"
"Aestuariimicrobium soli" "Colistin"
"Aestuariimicrobium soli" "Nalidixic acid"
"Aestuariimicrobium soli" "Polymyxin B"
"Aestuariimicrobium soli" "Temocillin"
"Agathobacter" "Aztreonam"
"Agathobacter" "Colistin"
"Agathobacter" "Nalidixic acid"
@ -11265,11 +11250,11 @@
"Amycolatopsis alba" "Nalidixic acid"
"Amycolatopsis alba" "Polymyxin B"
"Amycolatopsis alba" "Temocillin"
"Amycolatopsis albidiflava" "Aztreonam"
"Amycolatopsis albidiflava" "Colistin"
"Amycolatopsis albidiflava" "Nalidixic acid"
"Amycolatopsis albidiflava" "Polymyxin B"
"Amycolatopsis albidiflava" "Temocillin"
"Amycolatopsis albidoflava" "Aztreonam"
"Amycolatopsis albidoflava" "Colistin"
"Amycolatopsis albidoflava" "Nalidixic acid"
"Amycolatopsis albidoflava" "Polymyxin B"
"Amycolatopsis albidoflava" "Temocillin"
"Amycolatopsis albispora" "Aztreonam"
"Amycolatopsis albispora" "Colistin"
"Amycolatopsis albispora" "Nalidixic acid"
@ -22103,11 +22088,6 @@
"Catabacter honkongensis" "Nalidixic acid"
"Catabacter honkongensis" "Polymyxin B"
"Catabacter honkongensis" "Temocillin"
"Catabacteraceae" "Aztreonam"
"Catabacteraceae" "Colistin"
"Catabacteraceae" "Nalidixic acid"
"Catabacteraceae" "Polymyxin B"
"Catabacteraceae" "Temocillin"
"Catellatospora" "Aztreonam"
"Catellatospora" "Colistin"
"Catellatospora" "Nalidixic acid"
@ -23053,11 +23033,6 @@
"Chloroflexales" "Nalidixic acid"
"Chloroflexales" "Polymyxin B"
"Chloroflexales" "Temocillin"
"Chloroflexi" "Aztreonam"
"Chloroflexi" "Colistin"
"Chloroflexi" "Nalidixic acid"
"Chloroflexi" "Polymyxin B"
"Chloroflexi" "Temocillin"
"Chloroflexia" "Aztreonam"
"Chloroflexia" "Colistin"
"Chloroflexia" "Nalidixic acid"
@ -30752,12 +30727,6 @@
"Corynebacterium appendicis" "Nalidixic acid"
"Corynebacterium appendicis" "Polymyxin B"
"Corynebacterium appendicis" "Temocillin"
"Corynebacterium aquaticum" "Aztreonam"
"Corynebacterium aquaticum" "Colistin"
"Corynebacterium aquaticum" "Fosfomycin"
"Corynebacterium aquaticum" "Nalidixic acid"
"Corynebacterium aquaticum" "Polymyxin B"
"Corynebacterium aquaticum" "Temocillin"
"Corynebacterium aquatimens" "Aztreonam"
"Corynebacterium aquatimens" "Colistin"
"Corynebacterium aquatimens" "Fosfomycin"
@ -32711,11 +32680,6 @@
"Dehalococcoides mccartyi" "Nalidixic acid"
"Dehalococcoides mccartyi" "Polymyxin B"
"Dehalococcoides mccartyi" "Temocillin"
"Dehalococcoidetes" "Aztreonam"
"Dehalococcoidetes" "Colistin"
"Dehalococcoidetes" "Nalidixic acid"
"Dehalococcoidetes" "Polymyxin B"
"Dehalococcoidetes" "Temocillin"
"Dehalogenimonas" "Aztreonam"
"Dehalogenimonas" "Colistin"
"Dehalogenimonas" "Nalidixic acid"
@ -33146,11 +33110,6 @@
"Desulfofundulus thermobenzoicus thermobenzoicus" "Nalidixic acid"
"Desulfofundulus thermobenzoicus thermobenzoicus" "Polymyxin B"
"Desulfofundulus thermobenzoicus thermobenzoicus" "Temocillin"
"Desulfofundulus thermobenzoicus thermosyntrophicus" "Aztreonam"
"Desulfofundulus thermobenzoicus thermosyntrophicus" "Colistin"
"Desulfofundulus thermobenzoicus thermosyntrophicus" "Nalidixic acid"
"Desulfofundulus thermobenzoicus thermosyntrophicus" "Polymyxin B"
"Desulfofundulus thermobenzoicus thermosyntrophicus" "Temocillin"
"Desulfofundulus thermocisternus" "Aztreonam"
"Desulfofundulus thermocisternus" "Colistin"
"Desulfofundulus thermocisternus" "Nalidixic acid"
@ -60316,11 +60275,6 @@
"Geobacillus thermodenitrificans calidus" "Nalidixic acid"
"Geobacillus thermodenitrificans calidus" "Polymyxin B"
"Geobacillus thermodenitrificans calidus" "Temocillin"
"Geobacillus thermodenitrificans thermodenitrificans" "Aztreonam"
"Geobacillus thermodenitrificans thermodenitrificans" "Colistin"
"Geobacillus thermodenitrificans thermodenitrificans" "Nalidixic acid"
"Geobacillus thermodenitrificans thermodenitrificans" "Polymyxin B"
"Geobacillus thermodenitrificans thermodenitrificans" "Temocillin"
"Geobacillus thermoleovorans" "Aztreonam"
"Geobacillus thermoleovorans" "Colistin"
"Geobacillus thermoleovorans" "Nalidixic acid"
@ -62185,11 +62139,6 @@
"Haloechinothrix" "Nalidixic acid"
"Haloechinothrix" "Polymyxin B"
"Haloechinothrix" "Temocillin"
"Haloechinothrix aidingensis" "Aztreonam"
"Haloechinothrix aidingensis" "Colistin"
"Haloechinothrix aidingensis" "Nalidixic acid"
"Haloechinothrix aidingensis" "Polymyxin B"
"Haloechinothrix aidingensis" "Temocillin"
"Haloechinothrix alba" "Aztreonam"
"Haloechinothrix alba" "Colistin"
"Haloechinothrix alba" "Nalidixic acid"
@ -67423,20 +67372,13 @@
"Lactobacillus bombicola" "Teicoplanin"
"Lactobacillus bombicola" "Temocillin"
"Lactobacillus bombicola" "Vancomycin"
"Lactobacillus casei casei" "Aztreonam"
"Lactobacillus casei casei" "Colistin"
"Lactobacillus casei casei" "Nalidixic acid"
"Lactobacillus casei casei" "Polymyxin B"
"Lactobacillus casei casei" "Teicoplanin"
"Lactobacillus casei casei" "Temocillin"
"Lactobacillus casei casei" "Vancomycin"
"Lactobacillus casei pseudoplantarum" "Aztreonam"
"Lactobacillus casei pseudoplantarum" "Colistin"
"Lactobacillus casei pseudoplantarum" "Nalidixic acid"
"Lactobacillus casei pseudoplantarum" "Polymyxin B"
"Lactobacillus casei pseudoplantarum" "Teicoplanin"
"Lactobacillus casei pseudoplantarum" "Temocillin"
"Lactobacillus casei pseudoplantarum" "Vancomycin"
"Lactobacillus casei" "Aztreonam"
"Lactobacillus casei" "Colistin"
"Lactobacillus casei" "Nalidixic acid"
"Lactobacillus casei" "Polymyxin B"
"Lactobacillus casei" "Teicoplanin"
"Lactobacillus casei" "Temocillin"
"Lactobacillus casei" "Vancomycin"
"Lactobacillus colini" "Aztreonam"
"Lactobacillus colini" "Colistin"
"Lactobacillus colini" "Nalidixic acid"
@ -67731,13 +67673,13 @@
"Lactobacillus rogosae" "Teicoplanin"
"Lactobacillus rogosae" "Temocillin"
"Lactobacillus rogosae" "Vancomycin"
"Lactobacillus sakei sake" "Aztreonam"
"Lactobacillus sakei sake" "Colistin"
"Lactobacillus sakei sake" "Nalidixic acid"
"Lactobacillus sakei sake" "Polymyxin B"
"Lactobacillus sakei sake" "Teicoplanin"
"Lactobacillus sakei sake" "Temocillin"
"Lactobacillus sakei sake" "Vancomycin"
"Lactobacillus sakei" "Aztreonam"
"Lactobacillus sakei" "Colistin"
"Lactobacillus sakei" "Nalidixic acid"
"Lactobacillus sakei" "Polymyxin B"
"Lactobacillus sakei" "Teicoplanin"
"Lactobacillus sakei" "Temocillin"
"Lactobacillus sakei" "Vancomycin"
"Lactobacillus salivarius salivarius" "Aztreonam"
"Lactobacillus salivarius salivarius" "Colistin"
"Lactobacillus salivarius salivarius" "Nalidixic acid"
@ -68824,11 +68766,6 @@
"Lentzea albidocapillata" "Nalidixic acid"
"Lentzea albidocapillata" "Polymyxin B"
"Lentzea albidocapillata" "Temocillin"
"Lentzea albidocapillata albidocapillata" "Aztreonam"
"Lentzea albidocapillata albidocapillata" "Colistin"
"Lentzea albidocapillata albidocapillata" "Nalidixic acid"
"Lentzea albidocapillata albidocapillata" "Polymyxin B"
"Lentzea albidocapillata albidocapillata" "Temocillin"
"Lentzea albidocapillata violacea" "Aztreonam"
"Lentzea albidocapillata violacea" "Colistin"
"Lentzea albidocapillata violacea" "Nalidixic acid"
@ -75113,11 +75050,6 @@
"Mycobacterium intracellulare chimaera" "Nalidixic acid"
"Mycobacterium intracellulare chimaera" "Polymyxin B"
"Mycobacterium intracellulare chimaera" "Temocillin"
"Mycobacterium intracellulare intracellulare" "Aztreonam"
"Mycobacterium intracellulare intracellulare" "Colistin"
"Mycobacterium intracellulare intracellulare" "Nalidixic acid"
"Mycobacterium intracellulare intracellulare" "Polymyxin B"
"Mycobacterium intracellulare intracellulare" "Temocillin"
"Mycobacterium iranicum" "Aztreonam"
"Mycobacterium iranicum" "Colistin"
"Mycobacterium iranicum" "Nalidixic acid"
@ -75718,11 +75650,6 @@
"Mycoplasma cavipharyngis" "Nalidixic acid"
"Mycoplasma cavipharyngis" "Polymyxin B"
"Mycoplasma cavipharyngis" "Temocillin"
"Mycoplasma coccoides" "Aztreonam"
"Mycoplasma coccoides" "Colistin"
"Mycoplasma coccoides" "Nalidixic acid"
"Mycoplasma coccoides" "Polymyxin B"
"Mycoplasma coccoides" "Temocillin"
"Mycoplasma collis" "Aztreonam"
"Mycoplasma collis" "Colistin"
"Mycoplasma collis" "Nalidixic acid"
@ -78993,11 +78920,6 @@
"Nocardioides zhouii" "Nalidixic acid"
"Nocardioides zhouii" "Polymyxin B"
"Nocardioides zhouii" "Temocillin"
"Nocardiopsaceae" "Aztreonam"
"Nocardiopsaceae" "Colistin"
"Nocardiopsaceae" "Nalidixic acid"
"Nocardiopsaceae" "Polymyxin B"
"Nocardiopsaceae" "Temocillin"
"Nocardiopsis" "Aztreonam"
"Nocardiopsis" "Colistin"
"Nocardiopsis" "Nalidixic acid"
@ -80958,11 +80880,6 @@
"Paenibacillus graminis" "Nalidixic acid"
"Paenibacillus graminis" "Polymyxin B"
"Paenibacillus graminis" "Temocillin"
"Paenibacillus granivorans" "Aztreonam"
"Paenibacillus granivorans" "Colistin"
"Paenibacillus granivorans" "Nalidixic acid"
"Paenibacillus granivorans" "Polymyxin B"
"Paenibacillus granivorans" "Temocillin"
"Paenibacillus guangzhouensis" "Aztreonam"
"Paenibacillus guangzhouensis" "Colistin"
"Paenibacillus guangzhouensis" "Nalidixic acid"
@ -90318,78 +90235,6 @@
"Pseudomonas agarici" "Tedizolid"
"Pseudomonas agarici" "Vancomycin"
"Pseudomonas agarici" "Nafithromycin"
"Pseudomonas akapageensis" "Acetylmidecamycin"
"Pseudomonas akapageensis" "Acetylspiramycin"
"Pseudomonas akapageensis" "Avoparcin"
"Pseudomonas akapageensis" "Azithromycin"
"Pseudomonas akapageensis" "Cefacetrile"
"Pseudomonas akapageensis" "Cadazolid"
"Pseudomonas akapageensis" "Cefaclor"
"Pseudomonas akapageensis" "Cephradine"
"Pseudomonas akapageensis" "Cephalothin"
"Pseudomonas akapageensis" "Cefadroxil"
"Pseudomonas akapageensis" "Cefonicid"
"Pseudomonas akapageensis" "Clindamycin"
"Pseudomonas akapageensis" "Clarithromycin"
"Pseudomonas akapageensis" "Cefmetazole"
"Pseudomonas akapageensis" "Ceforanide"
"Pseudomonas akapageensis" "Cefprozil"
"Pseudomonas akapageensis" "Cefroxadine"
"Pseudomonas akapageensis" "Cefotiam"
"Pseudomonas akapageensis" "Ceftezole"
"Pseudomonas akapageensis" "Cefotetan"
"Pseudomonas akapageensis" "Cefatrizine"
"Pseudomonas akapageensis" "Cefuroxime axetil"
"Pseudomonas akapageensis" "Cefuroxime"
"Pseudomonas akapageensis" "Cycloserine"
"Pseudomonas akapageensis" "Cefazedone"
"Pseudomonas akapageensis" "Cefazolin"
"Pseudomonas akapageensis" "Dalbavancin"
"Pseudomonas akapageensis" "Dirithromycin"
"Pseudomonas akapageensis" "Erythromycin"
"Pseudomonas akapageensis" "Flurithromycin"
"Pseudomonas akapageensis" "Cefoxitin"
"Pseudomonas akapageensis" "Fusidic acid"
"Pseudomonas akapageensis" "Gamithromycin"
"Pseudomonas akapageensis" "Cephapirin"
"Pseudomonas akapageensis" "Josamycin"
"Pseudomonas akapageensis" "Kitasamycin"
"Pseudomonas akapageensis" "Cephalexin"
"Pseudomonas akapageensis" "Lincomycin"
"Pseudomonas akapageensis" "Linezolid"
"Pseudomonas akapageensis" "Loracarbef"
"Pseudomonas akapageensis" "Cefamandole"
"Pseudomonas akapageensis" "Miocamycin"
"Pseudomonas akapageensis" "Meleumycin"
"Pseudomonas akapageensis" "Midecamycin"
"Pseudomonas akapageensis" "Norvancomycin"
"Pseudomonas akapageensis" "Oleandomycin"
"Pseudomonas akapageensis" "Oritavancin"
"Pseudomonas akapageensis" "Benzylpenicillin"
"Pseudomonas akapageensis" "Pristinamycin"
"Pseudomonas akapageensis" "Pirlimycin"
"Pseudomonas akapageensis" "Primycin"
"Pseudomonas akapageensis" "Quinupristin/dalfopristin"
"Pseudomonas akapageensis" "Ramoplanin"
"Pseudomonas akapageensis" "Cefaloridine"
"Pseudomonas akapageensis" "Rifampicin"
"Pseudomonas akapageensis" "Rokitamycin"
"Pseudomonas akapageensis" "Roxithromycin"
"Pseudomonas akapageensis" "Solithromycin"
"Pseudomonas akapageensis" "Spiramycin"
"Pseudomonas akapageensis" "Teicoplanin"
"Pseudomonas akapageensis" "Thiacetazone"
"Pseudomonas akapageensis" "Tilmicosin"
"Pseudomonas akapageensis" "Tildipirosin"
"Pseudomonas akapageensis" "Telithromycin"
"Pseudomonas akapageensis" "Telavancin"
"Pseudomonas akapageensis" "Troleandomycin"
"Pseudomonas akapageensis" "Tulathromycin"
"Pseudomonas akapageensis" "Tylosin"
"Pseudomonas akapageensis" "Tylvalosin"
"Pseudomonas akapageensis" "Tedizolid"
"Pseudomonas akapageensis" "Vancomycin"
"Pseudomonas akapageensis" "Nafithromycin"
"Pseudomonas alcaligenes" "Acetylmidecamycin"
"Pseudomonas alcaligenes" "Acetylspiramycin"
"Pseudomonas alcaligenes" "Avoparcin"
@ -114794,36 +114639,6 @@
"Sanguibacter suarezii" "Nalidixic acid"
"Sanguibacter suarezii" "Polymyxin B"
"Sanguibacter suarezii" "Temocillin"
"Sanguibacteraceae" "Aztreonam"
"Sanguibacteraceae" "Colistin"
"Sanguibacteraceae" "Nalidixic acid"
"Sanguibacteraceae" "Polymyxin B"
"Sanguibacteraceae" "Temocillin"
"Sapromyces" "Aztreonam"
"Sapromyces" "Colistin"
"Sapromyces" "Nalidixic acid"
"Sapromyces" "Polymyxin B"
"Sapromyces" "Temocillin"
"Sapromyces androgynus" "Aztreonam"
"Sapromyces androgynus" "Colistin"
"Sapromyces androgynus" "Nalidixic acid"
"Sapromyces androgynus" "Polymyxin B"
"Sapromyces androgynus" "Temocillin"
"Sapromyces dubius" "Aztreonam"
"Sapromyces dubius" "Colistin"
"Sapromyces dubius" "Nalidixic acid"
"Sapromyces dubius" "Polymyxin B"
"Sapromyces dubius" "Temocillin"
"Sapromyces elongatus" "Aztreonam"
"Sapromyces elongatus" "Colistin"
"Sapromyces elongatus" "Nalidixic acid"
"Sapromyces elongatus" "Polymyxin B"
"Sapromyces elongatus" "Temocillin"
"Sapromyces indicus" "Aztreonam"
"Sapromyces indicus" "Colistin"
"Sapromyces indicus" "Nalidixic acid"
"Sapromyces indicus" "Polymyxin B"
"Sapromyces indicus" "Temocillin"
"Sarcina" "Aztreonam"
"Sarcina" "Colistin"
"Sarcina" "Nalidixic acid"
@ -120612,38 +120427,6 @@
"Streptococcus caprae" "Streptomycin"
"Streptococcus caprae" "Temocillin"
"Streptococcus caprae" "Tobramycin"
"Streptococcus casseliflavus" "Amikacin/fosfomycin"
"Streptococcus casseliflavus" "Amikacin"
"Streptococcus casseliflavus" "Apramycin"
"Streptococcus casseliflavus" "Arbekacin"
"Streptococcus casseliflavus" "Astromicin"
"Streptococcus casseliflavus" "Aztreonam"
"Streptococcus casseliflavus" "Bekanamycin"
"Streptococcus casseliflavus" "Ceftazidime"
"Streptococcus casseliflavus" "Colistin"
"Streptococcus casseliflavus" "Dibekacin"
"Streptococcus casseliflavus" "Framycetin"
"Streptococcus casseliflavus" "Fusidic acid"
"Streptococcus casseliflavus" "Gentamicin"
"Streptococcus casseliflavus" "Habekacin"
"Streptococcus casseliflavus" "Hygromycin"
"Streptococcus casseliflavus" "Isepamicin"
"Streptococcus casseliflavus" "Kanamycin/cephalexin"
"Streptococcus casseliflavus" "Kanamycin"
"Streptococcus casseliflavus" "Micronomicin"
"Streptococcus casseliflavus" "Nalidixic acid"
"Streptococcus casseliflavus" "Neomycin"
"Streptococcus casseliflavus" "Netilmicin"
"Streptococcus casseliflavus" "Pentisomicin"
"Streptococcus casseliflavus" "Propikacin"
"Streptococcus casseliflavus" "Polymyxin B"
"Streptococcus casseliflavus" "Plazomicin"
"Streptococcus casseliflavus" "Ribostamycin"
"Streptococcus casseliflavus" "Sisomicin"
"Streptococcus casseliflavus" "Streptoduocin"
"Streptococcus casseliflavus" "Streptomycin"
"Streptococcus casseliflavus" "Temocillin"
"Streptococcus casseliflavus" "Tobramycin"
"Streptococcus castoreus" "Amikacin/fosfomycin"
"Streptococcus castoreus" "Amikacin"
"Streptococcus castoreus" "Apramycin"
@ -124918,11 +124701,6 @@
"Streptomyces albospinus" "Nalidixic acid"
"Streptomyces albospinus" "Polymyxin B"
"Streptomyces albospinus" "Temocillin"
"Streptomyces albosporeus albosporeus" "Aztreonam"
"Streptomyces albosporeus albosporeus" "Colistin"
"Streptomyces albosporeus albosporeus" "Nalidixic acid"
"Streptomyces albosporeus albosporeus" "Polymyxin B"
"Streptomyces albosporeus albosporeus" "Temocillin"
"Streptomyces albosporeus labilomyceticus" "Aztreonam"
"Streptomyces albosporeus labilomyceticus" "Colistin"
"Streptomyces albosporeus labilomyceticus" "Nalidixic acid"
@ -125468,11 +125246,6 @@
"Streptomyces cavourensis" "Nalidixic acid"
"Streptomyces cavourensis" "Polymyxin B"
"Streptomyces cavourensis" "Temocillin"
"Streptomyces cavourensis cavourensis" "Aztreonam"
"Streptomyces cavourensis cavourensis" "Colistin"
"Streptomyces cavourensis cavourensis" "Nalidixic acid"
"Streptomyces cavourensis cavourensis" "Polymyxin B"
"Streptomyces cavourensis cavourensis" "Temocillin"
"Streptomyces cellostaticus" "Aztreonam"
"Streptomyces cellostaticus" "Colistin"
"Streptomyces cellostaticus" "Nalidixic acid"
@ -125548,11 +125321,6 @@
"Streptomyces chryseus" "Nalidixic acid"
"Streptomyces chryseus" "Polymyxin B"
"Streptomyces chryseus" "Temocillin"
"Streptomyces chrysomallus chrysomallus" "Aztreonam"
"Streptomyces chrysomallus chrysomallus" "Colistin"
"Streptomyces chrysomallus chrysomallus" "Nalidixic acid"
"Streptomyces chrysomallus chrysomallus" "Polymyxin B"
"Streptomyces chrysomallus chrysomallus" "Temocillin"
"Streptomyces chrysomallus fumigatus" "Aztreonam"
"Streptomyces chrysomallus fumigatus" "Colistin"
"Streptomyces chrysomallus fumigatus" "Nalidixic acid"
@ -125613,11 +125381,6 @@
"Streptomyces cinnamoneus albosporus" "Nalidixic acid"
"Streptomyces cinnamoneus albosporus" "Polymyxin B"
"Streptomyces cinnamoneus albosporus" "Temocillin"
"Streptomyces cinnamoneus cinnamoneus" "Aztreonam"
"Streptomyces cinnamoneus cinnamoneus" "Colistin"
"Streptomyces cinnamoneus cinnamoneus" "Nalidixic acid"
"Streptomyces cinnamoneus cinnamoneus" "Polymyxin B"
"Streptomyces cinnamoneus cinnamoneus" "Temocillin"
"Streptomyces cirratus" "Aztreonam"
"Streptomyces cirratus" "Colistin"
"Streptomyces cirratus" "Nalidixic acid"
@ -126088,11 +125851,6 @@
"Streptomyces globisporus" "Nalidixic acid"
"Streptomyces globisporus" "Polymyxin B"
"Streptomyces globisporus" "Temocillin"
"Streptomyces globisporus globisporus" "Aztreonam"
"Streptomyces globisporus globisporus" "Colistin"
"Streptomyces globisporus globisporus" "Nalidixic acid"
"Streptomyces globisporus globisporus" "Polymyxin B"
"Streptomyces globisporus globisporus" "Temocillin"
"Streptomyces globosus" "Aztreonam"
"Streptomyces globosus" "Colistin"
"Streptomyces globosus" "Nalidixic acid"
@ -126233,11 +125991,6 @@
"Streptomyces griseus" "Nalidixic acid"
"Streptomyces griseus" "Polymyxin B"
"Streptomyces griseus" "Temocillin"
"Streptomyces griseus griseus" "Aztreonam"
"Streptomyces griseus griseus" "Colistin"
"Streptomyces griseus griseus" "Nalidixic acid"
"Streptomyces griseus griseus" "Polymyxin B"
"Streptomyces griseus griseus" "Temocillin"
"Streptomyces guanduensis" "Aztreonam"
"Streptomyces guanduensis" "Colistin"
"Streptomyces guanduensis" "Nalidixic acid"
@ -126608,11 +126361,6 @@
"Streptomyces lavendulae grasserius" "Nalidixic acid"
"Streptomyces lavendulae grasserius" "Polymyxin B"
"Streptomyces lavendulae grasserius" "Temocillin"
"Streptomyces lavendulae lavendulae" "Aztreonam"
"Streptomyces lavendulae lavendulae" "Colistin"
"Streptomyces lavendulae lavendulae" "Nalidixic acid"
"Streptomyces lavendulae lavendulae" "Polymyxin B"
"Streptomyces lavendulae lavendulae" "Temocillin"
"Streptomyces lavenduligriseus" "Aztreonam"
"Streptomyces lavenduligriseus" "Colistin"
"Streptomyces lavenduligriseus" "Nalidixic acid"
@ -127883,11 +127631,6 @@
"Streptomyces thermoviolaceus apingens" "Nalidixic acid"
"Streptomyces thermoviolaceus apingens" "Polymyxin B"
"Streptomyces thermoviolaceus apingens" "Temocillin"
"Streptomyces thermoviolaceus thermoviolaceus" "Aztreonam"
"Streptomyces thermoviolaceus thermoviolaceus" "Colistin"
"Streptomyces thermoviolaceus thermoviolaceus" "Nalidixic acid"
"Streptomyces thermoviolaceus thermoviolaceus" "Polymyxin B"
"Streptomyces thermoviolaceus thermoviolaceus" "Temocillin"
"Streptomyces thermovulgaris" "Aztreonam"
"Streptomyces thermovulgaris" "Colistin"
"Streptomyces thermovulgaris" "Nalidixic acid"
@ -128463,36 +128206,11 @@
"Streptoverticillium" "Nalidixic acid"
"Streptoverticillium" "Polymyxin B"
"Streptoverticillium" "Temocillin"
"Streptoverticillium cinnamoneum lanosum" "Aztreonam"
"Streptoverticillium cinnamoneum lanosum" "Colistin"
"Streptoverticillium cinnamoneum lanosum" "Nalidixic acid"
"Streptoverticillium cinnamoneum lanosum" "Polymyxin B"
"Streptoverticillium cinnamoneum lanosum" "Temocillin"
"Streptoverticillium cinnamoneum sparsum" "Aztreonam"
"Streptoverticillium cinnamoneum sparsum" "Colistin"
"Streptoverticillium cinnamoneum sparsum" "Nalidixic acid"
"Streptoverticillium cinnamoneum sparsum" "Polymyxin B"
"Streptoverticillium cinnamoneum sparsum" "Temocillin"
"Streptoverticillium olivoreticuli olivoreticuli" "Aztreonam"
"Streptoverticillium olivoreticuli olivoreticuli" "Colistin"
"Streptoverticillium olivoreticuli olivoreticuli" "Nalidixic acid"
"Streptoverticillium olivoreticuli olivoreticuli" "Polymyxin B"
"Streptoverticillium olivoreticuli olivoreticuli" "Temocillin"
"Streptoverticillium reticulum protomycicum" "Aztreonam"
"Streptoverticillium reticulum protomycicum" "Colistin"
"Streptoverticillium reticulum protomycicum" "Nalidixic acid"
"Streptoverticillium reticulum protomycicum" "Polymyxin B"
"Streptoverticillium reticulum protomycicum" "Temocillin"
"Streptoverticillium verticillium quintum" "Aztreonam"
"Streptoverticillium verticillium quintum" "Colistin"
"Streptoverticillium verticillium quintum" "Nalidixic acid"
"Streptoverticillium verticillium quintum" "Polymyxin B"
"Streptoverticillium verticillium quintum" "Temocillin"
"Streptoverticillium verticillium tsukushiense" "Aztreonam"
"Streptoverticillium verticillium tsukushiense" "Colistin"
"Streptoverticillium verticillium tsukushiense" "Nalidixic acid"
"Streptoverticillium verticillium tsukushiense" "Polymyxin B"
"Streptoverticillium verticillium tsukushiense" "Temocillin"
"Streptoverticillium cinnamoneum" "Aztreonam"
"Streptoverticillium cinnamoneum" "Colistin"
"Streptoverticillium cinnamoneum" "Nalidixic acid"
"Streptoverticillium cinnamoneum" "Polymyxin B"
"Streptoverticillium cinnamoneum" "Temocillin"
"Subdoligranulum" "Aztreonam"
"Subdoligranulum" "Colistin"
"Subdoligranulum" "Nalidixic acid"
@ -129109,11 +128827,6 @@
"Tatumella terrea" "Tedizolid"
"Tatumella terrea" "Vancomycin"
"Tatumella terrea" "Nafithromycin"
"Tenericutes" "Aztreonam"
"Tenericutes" "Colistin"
"Tenericutes" "Nalidixic acid"
"Tenericutes" "Polymyxin B"
"Tenericutes" "Temocillin"
"Tenggerimyces" "Aztreonam"
"Tenggerimyces" "Colistin"
"Tenggerimyces" "Nalidixic acid"
@ -130753,11 +130466,6 @@
"Tropheryma whipplei" "Nalidixic acid"
"Tropheryma whipplei" "Polymyxin B"
"Tropheryma whipplei" "Temocillin"
"Tropherymataceae" "Aztreonam"
"Tropherymataceae" "Colistin"
"Tropherymataceae" "Nalidixic acid"
"Tropherymataceae" "Polymyxin B"
"Tropherymataceae" "Temocillin"
"Tropicihabitans" "Aztreonam"
"Tropicihabitans" "Colistin"
"Tropicihabitans" "Nalidixic acid"
@ -133267,11 +132975,6 @@
"Yaniella soli" "Nalidixic acid"
"Yaniella soli" "Polymyxin B"
"Yaniella soli" "Temocillin"
"Yaniellaceae" "Aztreonam"
"Yaniellaceae" "Colistin"
"Yaniellaceae" "Nalidixic acid"
"Yaniellaceae" "Polymyxin B"
"Yaniellaceae" "Temocillin"
"Yersinia" "Acetylmidecamycin"
"Yersinia" "Acetylspiramycin"
"Yersinia" "Avoparcin"

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@ -1,513 +0,0 @@
# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Data Analysis for R #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2022 Berends MS, Luz CF et al. #
# Developed at the University of Groningen, the Netherlands, in #
# collaboration with non-profit organisations Certe Medical #
# Diagnostics & Advice, and University Medical Center Groningen. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
# Go to https://lpsn.dsmz.de/downloads (register first) and download the latest CSV file.
file_location <- "data-raw/taxonomy.csv"
library(tidyverse)
library(AMR)
# these should still work after this update
test_fullname <- microorganisms$fullname
test_mo <- microorganisms$mo
# Helper functions --------------------------------------------------------
get_author_year <- function(ref) {
# Only keep first author, e.g. transform 'Smith, Jones, 2011' to 'Smith et al., 2011'
authors2 <- iconv(ref, from = "UTF-8", to = "ASCII//TRANSLIT")
authors2 <- gsub(" ?\\(Approved Lists [0-9]+\\) ?", " () ", authors2)
authors2 <- gsub(" [)(]+ $", "", authors2)
# remove leading and trailing brackets
authors2 <- trimws(gsub("^[(](.*)[)]$", "\\1", authors2))
# only take part after brackets if there's a name
authors2 <- ifelse(grepl(".*[)] [a-zA-Z]+.*", authors2),
gsub(".*[)] (.*)", "\\1", authors2),
authors2
)
# get year from last 4 digits
lastyear <- as.integer(gsub(".*([0-9]{4})$", "\\1", authors2))
# can never be later than now
lastyear <- ifelse(lastyear > as.integer(format(Sys.Date(), "%Y")),
NA,
lastyear
)
# get authors without last year
authors <- gsub("(.*)[0-9]{4}$", "\\1", authors2)
# remove nonsense characters from names
authors <- gsub("[^a-zA-Z,'& -]", "", authors)
# remove trailing and leading spaces
authors <- trimws(authors)
# only keep first author and replace all others by 'et al'
authors <- gsub("(,| and| et| &| ex| emend\\.?) .*", " et al.", authors)
# et al. always with ending dot
authors <- gsub(" et al\\.?", " et al.", authors)
authors <- gsub(" ?,$", "", authors)
# don't start with 'sensu' or 'ehrenb'
authors <- gsub("^(sensu|Ehrenb.?) ", "", authors, ignore.case = TRUE)
# no initials, only surname
authors <- gsub("^([A-Z]+ )+", "", authors, ignore.case = FALSE)
# combine author and year if year is available
ref <- ifelse(!is.na(lastyear),
paste0(authors, ", ", lastyear),
authors
)
# fix beginning and ending
ref <- gsub(", $", "", ref)
ref <- gsub("^, ", "", ref)
ref <- gsub("^(emend|et al.,?)", "", ref)
ref <- trimws(ref)
ref <- gsub("'", "", ref)
# a lot start with a lowercase character - fix that
ref[!grepl("^d[A-Z]", ref)] <- gsub("^([a-z])", "\\U\\1", ref[!grepl("^d[A-Z]", ref)], perl = TRUE)
# specific one for the French that are named dOrbigny
ref[grepl("^d[A-Z]", ref)] <- gsub("^d", "d'", ref[grepl("^d[A-Z]", ref)])
ref <- gsub(" +", " ", ref)
ref
}
df_remove_nonASCII <- function(df) {
# Remove non-ASCII characters (these are not allowed by CRAN)
df %>%
mutate_if(is.character, iconv, from = "UTF-8", to = "ASCII//TRANSLIT") %>%
# also remove invalid characters
mutate_if(is.character, ~ gsub("[\"'`]+", "", .)) %>%
AMR:::dataset_UTF8_to_ASCII()
}
abbreviate_mo <- function(x, minlength = 5, prefix = "", ...) {
# keep a starting Latin ae
suppressWarnings(
gsub("^ae", "\u00E6\u00E6", x, ignore.case = TRUE) %>%
abbreviate(
minlength = minlength,
use.classes = TRUE,
method = "both.sides", ...
) %>%
paste0(prefix, .) %>%
toupper() %>%
gsub("(\u00C6|\u00E6)+", "AE", .)
)
}
# Read data ---------------------------------------------------------------
taxonomy <- read_csv(file_location)
# Create synonyms ---------------------------------------------------------
new_synonyms <- taxonomy %>%
left_join(taxonomy,
by = c("record_lnk" = "record_no"),
suffix = c("", ".new")
) %>%
filter(!is.na(record_lnk)) %>%
mutate_all(~ ifelse(is.na(.), "", .)) %>%
transmute(
fullname = trimws(paste(genus_name, sp_epithet, subsp_epithet)),
fullname_new = trimws(paste(genus_name.new, sp_epithet.new, subsp_epithet.new)),
ref = get_author_year(authors),
prevalence = 0
) %>%
distinct(fullname, .keep_all = TRUE) %>%
filter(fullname != fullname_new) %>%
# this part joins this table to itself to correct for entries that had >1 renames,
# such as:
# Bacteroides tectum -> Bacteroides tectus -> Bacteroides pyogenes
left_join(., .,
by = c("fullname_new" = "fullname"),
suffix = c("", ".2")
) %>%
mutate(
fullname_new = ifelse(!is.na(fullname_new.2), fullname_new.2, fullname_new),
ref = ifelse(!is.na(ref.2), ref.2, ref)
) %>%
select(-ends_with(".2"))
mo_became_synonym <- microorganisms %>%
filter(fullname %in% new_synonyms$fullname)
updated_microorganisms <- taxonomy %>%
filter(is.na(record_lnk)) %>%
mutate_all(~ ifelse(is.na(.), "", .)) %>%
transmute(
mo = "",
fullname = trimws(paste(genus_name, sp_epithet, subsp_epithet)),
kingdom = "Bacteria",
phylum = "",
class = "",
order = "",
family = "",
genus = trimws(genus_name),
species = trimws(replace_na(sp_epithet, "")),
subspecies = trimws(replace_na(subsp_epithet, "")),
rank = case_when(
subspecies == "" & species == "" ~ "genus",
subspecies == "" ~ "species",
TRUE ~ "subsp."
),
ref = get_author_year(authors),
species_id = as.character(record_no),
source = "LPSN",
prevalence = 0,
snomed = NA
)
new_microorganisms <- updated_microorganisms %>%
filter(!fullname %in% microorganisms$fullname)
genera_with_mo_code <- updated_microorganisms %>%
filter(genus %in% (microorganisms %>% filter(kingdom == "Bacteria", rank == "genus") %>% pull(genus))) %>%
distinct(genus) %>%
left_join(microorganisms %>% filter(kingdom == "Bacteria", rank == "genus") %>% select(mo, genus),
by = "genus"
)
genera_without_mo_code <- updated_microorganisms %>%
filter(!genus %in% genera_with_mo_code$genus) %>%
pull(genus) %>%
unique()
genera_without_mo_code_abbr <- genera_without_mo_code %>% abbreviate_mo(5, prefix = "B_")
genera_without_mo_code_abbr[genera_without_mo_code_abbr %in% microorganisms$mo] <- abbreviate_mo(genera_without_mo_code[genera_without_mo_code_abbr %in% microorganisms$mo], 6, prefix = "B_")
genera_without_mo_code_abbr[genera_without_mo_code_abbr %in% microorganisms$mo] <- abbreviate_mo(genera_without_mo_code[genera_without_mo_code_abbr %in% microorganisms$mo], 7, prefix = "B_")
# all unique??
sum(genera_without_mo_code_abbr %in% microorganisms$mo) == 0
genus_abb <- tibble(
genus = genera_without_mo_code,
abbr = genera_without_mo_code_abbr
) %>%
bind_rows(microorganisms %>%
filter(kingdom == "Bacteria", rank == "genus", !genus %in% genera_without_mo_code) %>%
transmute(genus, abbr = as.character(mo))) %>%
arrange(genus)
# Update taxonomy ---------------------------------------------------------
# fill in the taxonomy of new genera
updated_taxonomy <- tibble(
phylum = character(0),
class = character(0),
order = character(0),
family = character(0),
genus = character(0)
)
for (page in LETTERS) {
message("Downloading page ", page, "... ", appendLF = FALSE)
url <- paste0("https://lpsn.dsmz.de/genus?page=", page)
x <- xml2::read_html(url) %>%
rvest::html_node(".main-list") %>%
# evety list element with a set <id> attribute
rvest::html_nodes("li[id]")
for (i in seq_len(length(x))) {
txt <- x %>%
magrittr::extract2(i) %>%
rvest::html_text() %>%
gsub("\\[[A-Za-z]+, no [a-z]+\\]", "NA", .) %>%
gsub("Candidatus ", "", ., fixed = TRUE) %>%
gsub("[ \t\r\n\"]+", "|", .) %>%
gsub("\\|ShowHide.*", "", .) %>%
gsub("[\\[\\]]", "", ., fixed = TRUE) %>%
gsub("^\\|", "", .) %>%
strsplit("|", fixed = TRUE) %>%
unlist()
txt[txt == "NA"] <- ""
txt <- gsub("[^A-Za-z]+", "", txt)
updated_taxonomy <- updated_taxonomy %>%
bind_rows(tibble(
phylum = txt[2],
class = txt[3],
order = txt[4],
family = txt[5],
genus = txt[6]
))
}
message(length(x), " entries (total ", nrow(updated_taxonomy), ")")
}
# Create new microorganisms -----------------------------------------------
new_microorganisms <- new_microorganisms %>%
left_join(genus_abb, by = "genus") %>%
group_by(genus) %>%
mutate(species_abb = abbreviate_mo(species, 4)) %>%
group_by(genus, species) %>%
mutate(subspecies_abb = abbreviate_mo(subspecies, 4)) %>%
ungroup() %>%
mutate(
mo = paste(abbr, species_abb, subspecies_abb, sep = "_"),
mo = gsub("_+$", "", mo)
) %>%
select(-matches("abb"))
# add taxonomy new microorganisms
MOs <- microorganisms %>%
mutate(mo = as.character(mo)) %>%
bind_rows(new_microorganisms) %>%
arrange(fullname)
# unique MO codes
MOs$mo[which(duplicated(MOs$mo))] <- paste0(MOs$mo[which(duplicated(MOs$mo))], 1)
# all unique?
!any(duplicated(MOs$mo))
MOs <- MOs %>%
# remove entries that are now a synonym
filter(!fullname %in% new_synonyms$fullname) %>%
# update the taxonomy
left_join(updated_taxonomy, by = "genus", suffix = c("", ".new")) %>%
mutate(
phylum = ifelse(!is.na(phylum.new), phylum.new, phylum),
class = ifelse(!is.na(class.new), class.new, class),
order = ifelse(!is.na(order.new), order.new, order),
family = ifelse(!is.na(family.new), family.new, family)
) %>%
select(-ends_with(".new")) %>%
# update prevalence based on taxonomy (Berends et al., 2021)
mutate(prevalence = case_when(
class == "Gammaproteobacteria" |
genus %in% c("Enterococcus", "Staphylococcus", "Streptococcus")
~ 1,
kingdom %in% c("Archaea", "Bacteria", "Chromista", "Fungi") &
(phylum %in% c(
"Proteobacteria",
"Firmicutes",
"Actinobacteria",
"Sarcomastigophora"
) |
genus %in% MO_PREVALENT_GENERA |
rank %in% c("kingdom", "phylum", "class", "order", "family"))
~ 2,
TRUE ~ 3
))
# add all mssing genera, families and orders
MOs <- MOs %>%
bind_rows(
MOs %>%
arrange(genus, species) %>%
distinct(genus, .keep_all = TRUE) %>%
filter(rank == "species", source != "manually added") %>%
mutate(
mo = gsub("^([A-Z]_[A-Z]+)_.*", "\\1", mo),
fullname = genus,
species = "",
subspecies = "",
rank = "genus",
species_id = "",
snomed = NA,
ref = NA_character_
),
MOs %>%
group_by(family) %>%
filter(!any(rank == "family") & n() > 1) %>%
ungroup() %>%
arrange(family) %>%
distinct(family, .keep_all = TRUE) %>%
filter(!family %in% c("", NA), source != "manually added") %>%
mutate(
mo = paste0(
substr(kingdom, 1, 1), "_[FAM]_",
abbreviate(family,
minlength = 8,
use.classes = TRUE,
method = "both.sides",
strict = FALSE
)
),
mo = toupper(mo),
fullname = family,
genus = "",
species = "",
subspecies = "",
rank = "family",
species_id = "",
snomed = NA,
ref = NA_character_
),
MOs %>%
group_by(order) %>%
filter(!any(rank == "order") & n() > 1) %>%
ungroup() %>%
arrange(order) %>%
distinct(order, .keep_all = TRUE) %>%
filter(!order %in% c("", NA), source != "manually added") %>%
mutate(
mo = paste0(
substr(kingdom, 1, 1), "_[ORD]_",
abbreviate(order,
minlength = 8,
use.classes = TRUE,
method = "both.sides",
strict = FALSE
)
),
mo = toupper(mo),
fullname = order,
family = "",
genus = "",
species = "",
subspecies = "",
rank = "order",
species_id = "",
snomed = NA,
ref = NA_character_
)
) %>%
arrange(fullname)
# clean up
MOs <- MOs %>%
df_remove_nonASCII()
# Add LPSN record IDs -----------------------------------------------------
records_ids <- taxonomy %>%
mutate(across(1:3, function(x) {
x[is.na(x)] <- ""
x
}),
fullname = trimws(paste(genus_name, sp_epithet, subsp_epithet))
) %>%
transmute(fullname, species_id = as.numeric(record_no)) %>%
arrange(fullname, species_id) %>%
distinct(fullname, .keep_all = TRUE)
message("Adding ", sum(records_ids$fullname %in% microorganisms$fullname), " LPSN record IDs")
MOs <- MOs %>%
select(-species_id) %>%
left_join(records_ids, by = "fullname") %>%
relocate(species_id, .after = ref) %>%
mutate(source = case_when(
!is.na(species_id) ~ "LPSN",
source %unlike% "manual" ~ "CoL",
TRUE ~ source
))
# Merge synonyms ----------------------------------------------------------
# remove synonyms that are now valid names
MOs.old <- microorganisms.old %>%
# add new synonyms
bind_rows(new_synonyms) %>%
filter(!fullname %in% MOs$fullname) %>%
arrange(fullname) %>%
distinct(fullname, fullname_new, .keep_all = TRUE) %>%
# add prevalence to old taxonomic names
select(-prevalence) %>%
left_join(MOs %>% select(fullname, prevalence), by = c("fullname_new" = "fullname")) %>%
# clean up
df_remove_nonASCII()
message("microorganisms new: ", sum(!MOs$fullname %in% c(microorganisms$fullname, MOs.old$fullname)))
message("microorganisms renamed: ", sum(!MOs.old$fullname %in% microorganisms.old$fullname))
# Save --------------------------------------------------------------------
# class <mo>
class(MOs$mo) <- c("mo", "character")
microorganisms <- MOs
microorganisms.old <- MOs.old
# --- Moraxella catarrhalis was named Branhamella catarrhalis (Catlin, 1970), but this is unaccepted in clinical microbiology
# we keep them both
microorganisms <- microorganisms %>%
bind_rows(microorganisms %>%
filter(fullname == "Branhamella catarrhalis") %>%
mutate(
mo = "B_MRXLL_CTRR",
fullname = "Moraxella catarrhalis",
genus = "Moraxella",
ref = "Henriksen et al., 1968",
species_id = "a374f6f0868e05f9c0f5077b60ee0a6c",
snomed = as.list(24226003)
)) %>%
arrange(fullname) %>%
df_remove_nonASCII()
microorganisms.old <- microorganisms.old %>%
filter(fullname != "Moraxella catarrhalis")
# ---
# (this would be a great moment to run data-raw/snomed.R as well)
# on the server, do:
usethis::use_data(microorganisms, overwrite = TRUE, version = 2, compress = "xz")
usethis::use_data(microorganisms.old, overwrite = TRUE, version = 2, compress = "xz")
rm(microorganisms)
rm(microorganisms.old)
# DON'T FORGET TO UPDATE R/globals.R!
# load new data sets
devtools::load_all(".")
# reset previously changed mo codes
rsi_translation$mo <- as.mo(rsi_translation$mo, language = NULL)
usethis::use_data(rsi_translation, overwrite = TRUE, version = 2, compress = "xz")
rm(rsi_translation)
microorganisms.codes$mo <- as.mo(microorganisms.codes$mo, language = NULL)
# new NAs introduced?
any(is.na(microorganisms.codes$mo))
usethis::use_data(microorganisms.codes, overwrite = TRUE, version = 2, compress = "xz")
rm(microorganisms.codes)
example_isolates$mo <- as.mo(example_isolates$mo, language = NULL)
usethis::use_data(example_isolates, overwrite = TRUE, version = 2)
rm(example_isolates)
intrinsic_resistant$microorganism <- suppressMessages(mo_name(intrinsic_resistant$microorganism))
usethis::use_data(intrinsic_resistant, overwrite = TRUE, version = 2)
rm(intrinsic_resistant)
# load new data sets again
devtools::load_all(".")
source("data-raw/_pre_commit_hook.R")
devtools::load_all(".")
# Test updates ------------------------------------------------------------
# and check: these codes should not be missing (will otherwise throw a unit test error):
AMR::microorganisms.codes %>% filter(!mo %in% MOs$mo)
AMR::rsi_translation %>% filter(!mo %in% MOs$mo)
AMR:::microorganisms.translation %>% filter(!mo_new %in% MOs$mo)
AMR::example_isolates %>% filter(!mo %in% MOs$mo)
# Don't forget to add SNOMED codes! (data-raw/snomed.R)
# run the unit tests
Sys.setenv(NOT_CRAN = "true")
testthat::test_file("tests/testthat/test-data.R")
testthat::test_file("tests/testthat/test-mo.R")
testthat::test_file("tests/testthat/test-mo_property.R")

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@ -1,77 +0,0 @@
# ==================================================================== #
# TITLE #
# Antimicrobial Resistance (AMR) Data Analysis for R #
# #
# SOURCE #
# https://github.com/msberends/AMR #
# #
# LICENCE #
# (c) 2018-2022 Berends MS, Luz CF et al. #
# Developed at the University of Groningen, the Netherlands, in #
# collaboration with non-profit organisations Certe Medical #
# Diagnostics & Advice, and University Medical Center Groningen. #
# #
# This R package is free software; you can freely use and distribute #
# it for both personal and commercial purposes under the terms of the #
# GNU General Public License version 2.0 (GNU GPL-2), as published by #
# the Free Software Foundation. #
# We created this package for both routine data analysis and academic #
# research and it was publicly released in the hope that it will be #
# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY. #
# #
# Visit our website for the full manual and a complete tutorial about #
# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== #
library(AMR)
library(tidyverse)
# we will use Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS)
# as a source, which copies directly from the latest US SNOMED CT version
# - go to https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009
# - check that current online version is higher than SNOMED_VERSION$current_version
# - if so, click on 'Download Value Set', choose 'TXT'
snomed <- read_tsv("data-raw/SNOMED_PHVS_Microorganism_CDC_V12.txt", skip = 3) %>%
select(1:2) %>%
set_names(c("snomed", "mo"))
# save all valid genera, species and subspecies
vctr <- unique(unlist(strsplit(c(microorganisms$fullname, microorganisms.old$fullname), " ")))
vctr <- tolower(vctr[vctr %like% "^[a-z]+$"])
# remove all parts of the name that are no valid values in genera, species or subspecies
# this takes ~20 seconds
snomed <- snomed %>%
mutate(
fullname = vapply(
FUN.VALUE = character(1),
# split on space and/or comma
strsplit(tolower(mo), "[ ,]"),
function(x) trimws(paste0(x[x %in% vctr], collapse = " "))
),
# remove " group"
fullname = gsub(" group", "", fullname, fixed = TRUE)
)
snomed_keep <- snomed %>%
filter(fullname %in% tolower(c(microorganisms$fullname, microorganisms.old$fullname))) %>%
group_by(fullname_lower = fullname) %>%
summarise(snomed = list(snomed))
message(nrow(snomed_keep), " MO's will get a SNOMED code.")
# save to microorganisms data set
microorganisms <- microorganisms %>%
# remove old snomed
select(-snomed) %>%
# create dummy var for joining
mutate(fullname_lower = tolower(fullname)) %>%
# join new snomed
left_join(snomed_keep) %>%
# remove dummy var
select(-fullname_lower) %>%
AMR:::dataset_UTF8_to_ASCII()
# don't forget to update the version number in SNOMED_VERSION in ./R/globals.R!
# usethis::use_data(microorganisms, overwrite = TRUE, version = 2, compress = "xz")

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4
data-raw/translations.tsv
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@ -32,8 +32,8 @@ biotype TRUE TRUE FALSE FALSE 生物型 biotype Biotyp βιότυπος biotip
vegetative TRUE TRUE FALSE FALSE 无性系 vegetativ vegetatief végétatif vegetativ βλαστικός vegetativo ([[ ]*?)グループ wegetatywna vegetativo вегетативный vegetativo vegetativ vejetatif вегетативний
([([ ]*?)group TRUE TRUE FALSE FALSE ([([]*?)组 \\1gruppe \\1groep \\1groupe \\1Gruppe ([([ ]*?)ομάδα \\1gruppo ([([ ]*?)grupa \\1grupo \\1группа \\1grupo \\1grupp ([([ ]*?)grup \\1група
([([ ]*?)Group TRUE TRUE FALSE FALSE ([([]*?)组 \\1Gruppe \\1Groep \\1Groupe \\1Gruppe ([([ ]*;)ομάδα \\1Gruppo ない ([([ ]*?)Grupa \\1Grupo \\1Группа \\1Grupo \\1Grupp ([([ ]*?)Grup \\1Група
no .*growth TRUE FALSE FALSE FALSE 无.*生长 ingen .*vækst geen .*groei pas .*croissance keine? .*wachstum όχι .*αύξηση sem .*crescimento 中間体 brak .*wzrostu sem .*crescimento отсутствие.*роста no .*crecimientonon ingen .*tillväxt büyüme yok відсутність .*росту
no|not TRUE FALSE FALSE FALSE 不|不 nej|ikke geen|niet non keine? no|not sem 感受性の高い、被ばく量の増加 nie|nie sem нет? no|sin nej|inte hayır|değil|hayir|degil ні
no .*growth FALSE FALSE FALSE FALSE 无.*生长 ingen .*vækst geen .*groei pas .*croissance keine(|n|m|r|s)|nicht .*wachstum όχι .*αύξηση sem .*crescimento 中間体 brak .*wzrostu sem .*crescimento отсутствие.*роста no .*crecimientonon ingen .*tillväxt büyüme yok відсутність .*росту
no|not FALSE FALSE FALSE FALSE 不|不 nej|ikke geen|niet non keine? no|not sem 感受性の高い、被ばく量の増加 nie|nie sem нет? no|sin nej|inte hayır|değil|hayir|degil ні
Intermediate TRUE FALSE FALSE FALSE 中级 Mellemliggende Intermediair Mittlere Ενδιάμεση 影響を受けやすい。 Pośrednia Intermedio Orta seviye Знижена чутливість
Susceptible, incr. exp. FALSE TRUE FALSE FALSE 易感，暴露增加 Modtagelig, øget eksp. Gevoelig bij verh. blootstelling Empfindlich, erh Belastung Ευάλωτος, αυξημένη έκθεση 影響を受けやすい Podatne, zwiększone narażenie Duyarlı, enk. maruziyet Чутливий до підвищеної експозиції
susceptible, incr. exp. FALSE TRUE FALSE FALSE 易感，接触增加 modtagelig, øget eksp. gevoelig bij verh. blootstelling empfindlich, erh Belastung Ευαίσθητος, αυξημένη έκθεση 曝露量増加 podatny, zwiększone narażenie duyarlı, enk. maruziyet чутливий до підвищеної експозиції

1 pattern regular_expr case_sensitive affect_ab_name affect_mo_name zh da nl fr de el it ja pl pt ru es sv tr uk
32 vegetative TRUE TRUE FALSE FALSE 无性系 vegetativ vegetatief végétatif vegetativ βλαστικός vegetativo ([[ ]*?)グループ wegetatywna vegetativo вегетативный vegetativo vegetativ vejetatif вегетативний
33 ([([ ]*?)group TRUE TRUE FALSE FALSE ([([]*?)组 \\1gruppe \\1groep \\1groupe \\1Gruppe ([([ ]*?)ομάδα \\1gruppo ([([ ]*?)grupa \\1grupo \\1группа \\1grupo \\1grupp ([([ ]*?)grup \\1група
34 ([([ ]*?)Group TRUE TRUE FALSE FALSE ([([]*?)组 \\1Gruppe \\1Groep \\1Groupe \\1Gruppe ([([ ]*;)ομάδα \\1Gruppo ない ([([ ]*?)Grupa \\1Grupo \\1Группа \\1Grupo \\1Grupp ([([ ]*?)Grup \\1Група
35 no .*growth TRUE FALSE FALSE FALSE FALSE 无.*生长 ingen .*vækst geen .*groei pas .*croissance keine? .*wachstum keine(|n|m|r|s)|nicht .*wachstum όχι .*αύξηση sem .*crescimento 中間体 brak .*wzrostu sem .*crescimento отсутствие.*роста no .*crecimientonon ingen .*tillväxt büyüme yok відсутність .*росту
36 no|not TRUE FALSE FALSE FALSE FALSE 不|不 nej|ikke geen|niet non keine? no|not sem 感受性の高い、被ばく量の増加 nie|nie sem нет? no|sin nej|inte hayır|değil|hayir|degil ні
37 Intermediate TRUE FALSE FALSE FALSE 中级 Mellemliggende Intermediair Mittlere Ενδιάμεση 影響を受けやすい。 Pośrednia Intermedio Orta seviye Знижена чутливість
38 Susceptible, incr. exp. FALSE TRUE FALSE FALSE 易感，暴露增加 Modtagelig, øget eksp. Gevoelig bij verh. blootstelling Empfindlich, erh Belastung Ευάλωτος, αυξημένη έκθεση 影響を受けやすい Podatne, zwiększone narażenie Duyarlı, enk. maruziyet Чутливий до підвищеної експозиції
39 susceptible, incr. exp. FALSE TRUE FALSE FALSE 易感，接触增加 modtagelig, øget eksp. gevoelig bij verh. blootstelling empfindlich, erh Belastung Ευαίσθητος, αυξημένη έκθεση 曝露量増加 podatny, zwiększone narażenie duyarlı, enk. maruziyet чутливий до підвищеної експозиції

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@ -4,7 +4,7 @@
The `AMR` package is a [free and open-source](#copyright) R package with [zero dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. The rationale behind this package was scientifically described in the Journal of Statistical Software (Volume xx, Issue xx; [DOI 10.18637/jss.v000.i00](https://doi.org/10.18637/jss.v000.i00) *- waiting for copy-editing to finish*).
After installing this package, R knows [**~71,000 distinct microbial species**](./reference/microorganisms.html) and all [**~570 antibiotic, antimycotic and antiviral drugs**](./reference/antibiotics.html) by name and code (including ATC, WHONET/EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl). This R package formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)) but is [actively and durably maintained](./news) by two public healthcare organisations in the Netherlands.
After installing this package, R knows [**~63,000 distinct microbial species**](./reference/microorganisms.html) and all [**~570 antibiotic, antimycotic and antiviral drugs**](./reference/antibiotics.html) by name and code (including ATC, WHONET/EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl). This R package formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)) but is [actively and durably maintained](./news) by two public healthcare organisations in the Netherlands.
##### Used in 175 countries, translated to 16 languages
@ -106,7 +106,7 @@ out %>% set_ab_names(property = "atc")
This package was intended as a comprehensive toolbox for integrated AMR data analysis. This package can be used for:
* Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the [Catalogue of Life](http://www.catalogueoflife.org) and [List of Prokaryotic names with Standing in Nomenclature](https://lpsn.dsmz.de) ([manual](./reference/mo_property.html))
* Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the List of Prokaryotic names with Standing in Nomenclature ([LPSN]((https://lpsn.dsmz.de))) and the Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) ([manual](./reference/mo_property.html))
* Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines ([manual](./reference/as.rsi.html))
* Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records ([manual](./reference/ab_from_text.html))
* Determining first isolates to be used for AMR data analysis ([manual](./reference/first_isolate.html))

4
man/AMR.Rd
View File

@ -9,13 +9,13 @@ Welcome to the \code{AMR} package.
\details{
\code{AMR} is a free, open-source and independent \R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
After installing this package, \R knows ~71,000 distinct microbial species and all ~570 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
After installing this package, \R knows ~63,000 distinct microbial species and all ~570 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-NET, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
This package is fully independent of any other \R package and works on Windows, macOS and Linux with all versions of \R since R-3.0.0 (April 2013). It was designed to work in any setting, including those with very limited resources. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the University of Groningen, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. This \R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation.
This package can be used for:
\itemize{
\item Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life and List of Prokaryotic names with Standing in Nomenclature
\item Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF)
\item Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines
\item Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records
\item Determining first isolates to be used for AMR data analysis

94
man/as.mo.Rd
View File

@ -6,6 +6,7 @@
\alias{is.mo}
\alias{mo_failures}
\alias{mo_uncertainties}
\alias{mo_reset_session}
\alias{mo_renamed}
\title{Transform Input to a Microorganism Code}
\usage{
@ -13,7 +14,9 @@ as.mo(
x,
Becker = FALSE,
Lancefield = FALSE,
minimum_matching_score = NULL,
allow_uncertain = TRUE,
keep_synonyms = FALSE,
reference_df = get_mo_source(),
ignore_pattern = getOption("AMR_ignore_pattern"),
language = get_AMR_locale(),
@ -27,6 +30,8 @@ mo_failures()
mo_uncertainties()
mo_reset_session()
mo_renamed()
}
\arguments{
@ -40,6 +45,8 @@ This excludes \emph{Staphylococcus aureus} at default, use \code{Becker = "all"}
This excludes enterococci at default (who are in group D), use \code{Lancefield = "all"} to also categorise all enterococci as group D.}
\item{minimum_matching_score}{a numeric value to set as the lower limit for the \link[=mo_matching_score]{MO matching score}. When left blank, this will be determined automatically based on the character length of \code{x}, its \link[=microorganisms]{taxonomic kingdom} and \link[=mo_matching_score]{human pathogenicity}.}
\item{allow_uncertain}{a number between \code{0} (or \code{"none"}) and \code{3} (or \code{"all"}), or \code{TRUE} (= \code{2}) or \code{FALSE} (= \code{0}) to indicate whether the input should be checked for less probable results, see \emph{Details}}
\item{reference_df}{a \link{data.frame} to be used for extra reference when translating \code{x} to a valid \code{\link{mo}}. See \code{\link[=set_mo_source]{set_mo_source()}} and \code{\link[=get_mo_source]{get_mo_source()}} to automate the usage of your own codes (e.g. used in your analysis or organisation).}
@ -56,7 +63,7 @@ This excludes enterococci at default (who are in group D), use \code{Lancefield
A \link{character} \link{vector} with additional class \code{\link{mo}}
}
\description{
Use this function to determine a valid microorganism code (\code{\link{mo}}). Determination is done using intelligent rules and the complete taxonomic kingdoms Bacteria, Chromista, Protozoa, Archaea and most microbial species from the kingdom Fungi (see \emph{Source}). The input can be almost anything: a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (such as \code{"S. aureus"}), an abbreviation known in the field (such as \code{"MRSA"}), or just a genus. See \emph{Examples}.
Use this function to determine a valid microorganism code (\code{\link{mo}}). Determination is done using intelligent rules and the complete taxonomic kingdoms Animalia, Archaea, Bacteria, Plantae and Protozoa, and most microbial species from the kingdom Fungi (see \emph{Source}). The input can be almost anything: a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (such as \code{"S. aureus"}), an abbreviation known in the field (such as \code{"MRSA"}), or just a genus. See \emph{Examples}.
}
\details{
\subsection{General Info}{
@ -74,14 +81,14 @@ A microorganism (MO) code from this package (class: \code{\link{mo}}) is human r
| | \\----> species, a 4-5 letter acronym
| \\----> genus, a 5-7 letter acronym
\\----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria),
C (Chromista), F (Fungi), P (Protozoa)
F (Fungi), PL (Plantae), P (Protozoa)
}\if{html}{\out{</div>}}
Values that cannot be coerced will be considered 'unknown' and will get the MO code \code{UNKNOWN}.
Use the \code{\link[=mo_property]{mo_*}} functions to get properties based on the returned code, see \emph{Examples}.
The algorithm uses data from the Catalogue of Life (see below) and from one other source (see \link{microorganisms}).
The algorithm uses data from the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF) (see \link{microorganisms}).
The \code{\link[=as.mo]{as.mo()}} function uses several coercion rules for fast and logical results. It assesses the input matching criteria in the following order:
\enumerate{
@ -122,19 +129,20 @@ There are three helper functions that can be run after using the \code{\link[=as
\subsection{Microbial Prevalence of Pathogens in Humans}{
The intelligent rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the \code{prevalence} columns in the \link{microorganisms} and \link{microorganisms.old} data sets. The grouping into human pathogenic prevalence is explained in the section \emph{Matching Score for Microorganisms} below.
The coercion rules consider the prevalence of microorganisms in humans grouped into three groups, which is available as the \code{prevalence} columns in the \link{microorganisms} data set. The grouping into human pathogenic prevalence is explained in the section \emph{Matching Score for Microorganisms} below.
}
}
\section{Source}{
\enumerate{
\item Becker K \emph{et al.} \strong{Coagulase-Negative Staphylococci}. 2014. Clin Microbiol Rev. 27(4): 870-926; \doi{10.1128/CMR.00109-13}
\item Becker K \emph{et al.} \strong{Implications of identifying the recently defined members of the \emph{S. aureus} complex, \emph{S. argenteus} and \emph{S. schweitzeri}: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).} 2019. Clin Microbiol Infect; \doi{10.1016/j.cmi.2019.02.028}
\item Becker K \emph{et al.} \strong{Emergence of coagulase-negative staphylococci} 2020. Expert Rev Anti Infect Ther. 18(4):349-366; \doi{10.1080/14787210.2020.1730813}
\item Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571-95; \doi{10.1084/jem.57.4.571}
\item Catalogue of Life: 2019 Annual Checklist, \url{http://www.catalogueoflife.org}
\item List of Prokaryotic names with Standing in Nomenclature (5 October 2021), \doi{10.1099/ijsem.0.004332}
\item US Edition of SNOMED CT from 1 September 2020, retrieved from the Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS), OID 2.16.840.1.114222.4.11.1009, version 12; url: \url{https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009}
\item Becker K. \emph{et al.} (2014). \strong{Coagulase-Negative Staphylococci.} \emph{Clin Microbiol Rev.} 27(4): 870-926; \doi{10.1128/CMR.00109-13}
\item Becker K. \emph{et al.} (2019). \strong{Implications of identifying the recently defined members of the \emph{S. aureus} complex, \emph{S. argenteus} and \emph{S. schweitzeri}: A position paper of members of the ESCMID Study Group for staphylococci and Staphylococcal Diseases (ESGS).} \emph{Clin Microbiol Infect}; \doi{10.1016/j.cmi.2019.02.028}
\item Becker K. \emph{et al.} (2020). \strong{Emergence of coagulase-negative staphylococci} \emph{Expert Rev Anti Infect Ther.} 18(4):349-366; \doi{10.1080/14787210.2020.1730813}
\item Lancefield R.C. (1933). \strong{A serological differentiation of human and other groups of hemolytic streptococci}. \emph{J Exp Med.} 57(4): 571-95; \doi{10.1084/jem.57.4.571}
\item Berends M.S. \emph{et al.} (2022). \strong{Trends in Occurrence and Phenotypic Resistance of Coagulase-Negative Staphylococci (CoNS) Found in Human Blood in the Northern Netherlands between 2013 and 2019} \emph{Microorganisms} 10(9), 1801; \doi{10.3390/microorganisms10091801}
\item Parte, A.C. \emph{et al.} (2020). \strong{List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ.} International Journal of Systematic and Evolutionary Microbiology, 70, 5607-5612; \doi{10.1099/ijsem.0.004332}. Accessed from \url{https://lpsn.dsmz.de} on 12 September, 2022.
\item GBIF Secretariat (November 26, 2021). GBIF Backbone Taxonomy. Checklist dataset \doi{10.15468/39omei}. Accessed from \url{https://www.gbif.org} on 12 September, 2022.
\item Public Health Information Network Vocabulary Access and Distribution System (PHIN VADS). US Edition of SNOMED CT from 1 September 2020. Value Set Name 'Microoganism', OID 2.16.840.1.114222.4.11.1009 (v12). URL: \url{https://phinvads.cdc.gov}
}
}
@ -154,21 +162,17 @@ where:
\item \ifelse{html}{\out{<i>k<sub>n</sub></i> is the taxonomic kingdom of <i>n</i>, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.}}{l_n is the taxonomic kingdom of \eqn{n}, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.}
}
The grouping into human pathogenic prevalence (\eqn{p}) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence. \strong{Group 1} (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is \emph{Enterococcus}, \emph{Staphylococcus} or \emph{Streptococcus}. This group consequently contains all common Gram-negative bacteria, such as \emph{Pseudomonas} and \emph{Legionella} and all species within the order Enterobacterales. \strong{Group 2} consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is \emph{Absidia}, \emph{Acremonium}, \emph{Actinotignum}, \emph{Alternaria}, \emph{Anaerosalibacter}, \emph{Apophysomyces}, \emph{Arachnia}, \emph{Aspergillus}, \emph{Aureobacterium}, \emph{Aureobasidium}, \emph{Bacteroides}, \emph{Basidiobolus}, \emph{Beauveria}, \emph{Blastocystis}, \emph{Branhamella}, \emph{Calymmatobacterium}, \emph{Candida}, \emph{Capnocytophaga}, \emph{Catabacter}, \emph{Chaetomium}, \emph{Chryseobacterium}, \emph{Chryseomonas}, \emph{Chrysonilia}, \emph{Cladophialophora}, \emph{Cladosporium}, \emph{Conidiobolus}, \emph{Cryptococcus}, \emph{Curvularia}, \emph{Exophiala}, \emph{Exserohilum}, \emph{Flavobacterium}, \emph{Fonsecaea}, \emph{Fusarium}, \emph{Fusobacterium}, \emph{Hendersonula}, \emph{Hypomyces}, \emph{Koserella}, \emph{Lelliottia}, \emph{Leptosphaeria}, \emph{Leptotrichia}, \emph{Malassezia}, \emph{Malbranchea}, \emph{Mortierella}, \emph{Mucor}, \emph{Mycocentrospora}, \emph{Mycoplasma}, \emph{Nectria}, \emph{Ochroconis}, \emph{Oidiodendron}, \emph{Phoma}, \emph{Piedraia}, \emph{Pithomyces}, \emph{Pityrosporum}, \emph{Prevotella}, \emph{Pseudallescheria}, \emph{Rhizomucor}, \emph{Rhizopus}, \emph{Rhodotorula}, \emph{Scolecobasidium}, \emph{Scopulariopsis}, \emph{Scytalidium}, \emph{Sporobolomyces}, \emph{Stachybotrys}, \emph{Stomatococcus}, \emph{Treponema}, \emph{Trichoderma}, \emph{Trichophyton}, \emph{Trichosporon}, \emph{Tritirachium} or \emph{Ureaplasma}. \strong{Group 3} consists of all other microorganisms.
The grouping into human pathogenic prevalence (\eqn{p}) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence:
\strong{Group 1} (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is \emph{Enterococcus}, \emph{Staphylococcus} or \emph{Streptococcus}. This group consequently contains all common Gram-negative bacteria, such as \emph{Pseudomonas} and \emph{Legionella} and all species within the order Enterobacterales.
\strong{Group 2} consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is \emph{Absidia}, \emph{Acanthamoeba}, \emph{Acholeplasma}, \emph{Acremonium}, \emph{Actinotignum}, \emph{Aedes}, \emph{Alistipes}, \emph{Alloprevotella}, \emph{Alternaria}, \emph{Amoeba}, \emph{Anaerosalibacter}, \emph{Ancylostoma}, \emph{Angiostrongylus}, \emph{Anisakis}, \emph{Anopheles}, \emph{Apophysomyces}, \emph{Arachnia}, \emph{Aspergillus}, \emph{Aureobasidium}, \emph{Bacteroides}, \emph{Basidiobolus}, \emph{Beauveria}, \emph{Bergeyella}, \emph{Blastocystis}, \emph{Blastomyces}, \emph{Borrelia}, \emph{Brachyspira}, \emph{Branhamella}, \emph{Butyricimonas}, \emph{Candida}, \emph{Capillaria}, \emph{Capnocytophaga}, \emph{Catabacter}, \emph{Cetobacterium}, \emph{Chaetomium}, \emph{Chlamydia}, \emph{Chlamydophila}, \emph{Chryseobacterium}, \emph{Chrysonilia}, \emph{Cladophialophora}, \emph{Cladosporium}, \emph{Conidiobolus}, \emph{Contracaecum}, \emph{Cordylobia}, \emph{Cryptococcus}, \emph{Curvularia}, \emph{Deinococcus}, \emph{Demodex}, \emph{Dermatobia}, \emph{Dientamoeba}, \emph{Diphyllobothrium}, \emph{Dirofilaria}, \emph{Dysgonomonas}, \emph{Echinostoma}, \emph{Elizabethkingia}, \emph{Empedobacter}, \emph{Entamoeba}, \emph{Enterobius}, \emph{Exophiala}, \emph{Exserohilum}, \emph{Fasciola}, \emph{Flavobacterium}, \emph{Fonsecaea}, \emph{Fusarium}, \emph{Fusobacterium}, \emph{Giardia}, \emph{Haloarcula}, \emph{Halobacterium}, \emph{Halococcus}, \emph{Hendersonula}, \emph{Heterophyes}, \emph{Histomonas}, \emph{Histoplasma}, \emph{Hymenolepis}, \emph{Hypomyces}, \emph{Hysterothylacium}, \emph{Leishmania}, \emph{Lelliottia}, \emph{Leptosphaeria}, \emph{Leptotrichia}, \emph{Lucilia}, \emph{Lumbricus}, \emph{Malassezia}, \emph{Malbranchea}, \emph{Metagonimus}, \emph{Microsporidium}, \emph{Microsporum}, \emph{Mortierella}, \emph{Mucor}, \emph{Mycocentrospora}, \emph{Mycoplasma}, \emph{Myroides}, \emph{Necator}, \emph{Nectria}, \emph{Ochroconis}, \emph{Odoribacter}, \emph{Oesophagostomum}, \emph{Oidiodendron}, \emph{Opisthorchis}, \emph{Ornithobacterium}, \emph{Parabacteroides}, \emph{Pediculus}, \emph{Pedobacter}, \emph{Phlebotomus}, \emph{Phocaeicola}, \emph{Phocanema}, \emph{Phoma}, \emph{Piedraia}, \emph{Pithomyces}, \emph{Pityrosporum}, \emph{Porphyromonas}, \emph{Prevotella}, \emph{Pseudallescheria}, \emph{Pseudoterranova}, \emph{Pulex}, \emph{Rhizomucor}, \emph{Rhizopus}, \emph{Rhodotorula}, \emph{Riemerella}, \emph{Saccharomyces}, \emph{Sarcoptes}, \emph{Scolecobasidium}, \emph{Scopulariopsis}, \emph{Scytalidium}, \emph{Sphingobacterium}, \emph{Spirometra}, \emph{Spiroplasma}, \emph{Sporobolomyces}, \emph{Stachybotrys}, \emph{Streptobacillus}, \emph{Strongyloides}, \emph{Syngamus}, \emph{Taenia}, \emph{Tannerella}, \emph{Tenacibaculum}, \emph{Terrimonas}, \emph{Toxocara}, \emph{Treponema}, \emph{Trichinella}, \emph{Trichobilharzia}, \emph{Trichoderma}, \emph{Trichomonas}, \emph{Trichophyton}, \emph{Trichosporon}, \emph{Trichostrongylus}, \emph{Trichuris}, \emph{Tritirachium}, \emph{Trombicula}, \emph{Trypanosoma}, \emph{Tunga}, \emph{Ureaplasma}, \emph{Victivallis}, \emph{Wautersiella}, \emph{Weeksella} or \emph{Wuchereria}.
\strong{Group 3} consists of all other microorganisms.
All characters in \eqn{x} and \eqn{n} are ignored that are other than A-Z, a-z, 0-9, spaces and parentheses.
All matches are sorted descending on their matching score and for all user input values, the top match will be returned. This will lead to the effect that e.g., \code{"E. coli"} will return the microbial ID of \emph{Escherichia coli} (\eqn{m = 0.688}, a highly prevalent microorganism found in humans) and not \emph{Entamoeba coli} (\eqn{m = 0.079}, a less prevalent microorganism in humans), although the latter would alphabetically come first.
Since \code{AMR} version 1.8.1, common microorganism abbreviations are ignored in determining the matching score. These abbreviations are currently: AIEC, ATEC, BORSA, CRSM, DAEC, EAEC, EHEC, EIEC, EPEC, ETEC, GISA, MRPA, MRSA, MRSE, MSSA, MSSE, NMEC, PISP, PRSP, STEC, UPEC, VISA, VISP, VRE, VRSA and VRSP.
}
\section{Catalogue of Life}{
\if{html}{\figure{logo_col.png}{options: height="40" style=margin-bottom:"5"} \cr}
This package contains the complete taxonomic tree of almost all microorganisms (~71,000 species) from the authoritative and comprehensive Catalogue of Life (CoL, \url{http://www.catalogueoflife.org}). The CoL is the most comprehensive and authoritative global index of species currently available. Nonetheless, we supplemented the CoL data with data from the List of Prokaryotic names with Standing in Nomenclature (LPSN, \href{https://lpsn.dsmz.de}{lpsn.dsmz.de}). This supplementation is needed until the \href{https://github.com/CatalogueOfLife/general}{CoL+ project} is finished, which we await.
\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which versions of the CoL and LPSN were included in this package with \code{\link[=catalogue_of_life_version]{catalogue_of_life_version()}}.
}
\section{Reference Data Publicly Available}{
@ -179,29 +183,31 @@ All data sets in this \code{AMR} package (about microorganisms, antibiotics, R/S
\examples{
\donttest{
# These examples all return "B_STPHY_AURS", the ID of S. aureus:
as.mo("sau") # WHONET code
as.mo("stau")
as.mo("STAU")
as.mo("staaur")
as.mo("S. aureus")
as.mo("S aureus")
as.mo("Staphylococcus aureus")
as.mo("Staphylococcus aureus (MRSA)")
as.mo("Zthafilokkoockus oureuz") # handles incorrect spelling
as.mo("MRSA") # Methicillin Resistant S. aureus
as.mo("VISA") # Vancomycin Intermediate S. aureus
as.mo("VRSA") # Vancomycin Resistant S. aureus
as.mo(115329001) # SNOMED CT code
as.mo(c(
"sau", # WHONET code
"stau",
"STAU",
"staaur",
"S. aureus",
"S aureus",
"Staphylococcus aureus",
"Staphylococcus aureus (MRSA,",
"Zthafilokkoockus oureuz", # handles incorrect spelling
"MRSA", # Methicillin Resistant S. aureus
"VISA", # Vancomycin Intermediate S. aureus
"VRSA", # Vancomycin Resistant S. aureus
115329001
)) # SNOMED CT code
# Dyslexia is no problem - these all work:
as.mo("Ureaplasma urealyticum")
as.mo("Ureaplasma urealyticus")
as.mo("Ureaplasmium urealytica")
as.mo("Ureaplazma urealitycium")
as.mo(c(
"Ureaplasma urealyticum",
"Ureaplasma urealyticus",
"Ureaplasmium urealytica",
"Ureaplazma urealitycium"
))
as.mo("Streptococcus group A")
as.mo("GAS") # Group A Streptococci
as.mo("GBS") # Group B Streptococci
as.mo("S. epidermidis") # will remain species: B_STPHY_EPDR
as.mo("S. epidermidis", Becker = TRUE) # will not remain species: B_STPHY_CONS
@ -210,9 +216,9 @@ as.mo("S. pyogenes") # will remain species: B_STRPT_PYGN
as.mo("S. pyogenes", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA
# All mo_* functions use as.mo() internally too (see ?mo_property):
mo_genus("E. coli") # returns "Escherichia"
mo_gramstain("E. coli") # returns "Gram negative"
mo_is_intrinsic_resistant("E. coli", "vanco") # returns TRUE
mo_genus("Esch coli")
mo_gramstain("E. coli")
mo_is_intrinsic_resistant("E. coli", "vanco")
}
}
\seealso{

58
man/catalogue_of_life.Rd
View File

@ -1,58 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/catalogue_of_life.R
\name{catalogue_of_life}
\alias{catalogue_of_life}
\title{The Catalogue of Life}
\description{
This package contains the complete taxonomic tree (last updated: 5 October 2021) of almost all microorganisms from the authoritative and comprehensive Catalogue of Life (CoL), supplemented with data from the List of Prokaryotic names with Standing in Nomenclature (LPSN).
}
\section{Catalogue of Life}{
\if{html}{\figure{logo_col.png}{options: height="40" style=margin-bottom:"5"} \cr}
This package contains the complete taxonomic tree of almost all microorganisms (~71,000 species) from the authoritative and comprehensive Catalogue of Life (CoL, \url{http://www.catalogueoflife.org}). The CoL is the most comprehensive and authoritative global index of species currently available. Nonetheless, we supplemented the CoL data with data from the List of Prokaryotic names with Standing in Nomenclature (LPSN, \href{https://lpsn.dsmz.de}{lpsn.dsmz.de}). This supplementation is needed until the \href{https://github.com/CatalogueOfLife/general}{CoL+ project} is finished, which we await.
\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which versions of the CoL and LPSN were included in this package with \code{\link[=catalogue_of_life_version]{catalogue_of_life_version()}}.
}
\section{Included Taxa}{
Included are:
\itemize{
\item All ~58,000 (sub)species from the kingdoms of Archaea, Bacteria, Chromista and Protozoa
\item All ~5,000 (sub)species from these orders of the kingdom of Fungi: Eurotiales, Microascales, Mucorales, Onygenales, Pneumocystales, Saccharomycetales, Schizosaccharomycetales and Tremellales, as well as ~4,600 other fungal (sub)species. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package and including everything would tremendously slow down our algorithms too. By only including the aforementioned taxonomic orders, the most relevant fungi are covered (such as all species of \emph{Aspergillus}, \emph{Candida}, \emph{Cryptococcus}, \emph{Histplasma}, \emph{Pneumocystis}, \emph{Saccharomyces} and \emph{Trichophyton}).
\item All ~2,200 (sub)species from ~50 other relevant genera from the kingdom of Animalia (such as \emph{Strongyloides} and \emph{Taenia})
\item All ~14,000 previously accepted names of all included (sub)species (these were taxonomically renamed)
\item The complete taxonomic tree of all included (sub)species: from kingdom to subspecies
\item The responsible author(s) and year of scientific publication
}
The Catalogue of Life (\url{http://www.catalogueoflife.org}) is the most comprehensive and authoritative global index of species currently available. It holds essential information on the names, relationships and distributions of over 1.9 million species. The Catalogue of Life is used to support the major biodiversity and conservation information services such as the Global Biodiversity Information Facility (GBIF), Encyclopedia of Life (EoL) and the International Union for Conservation of Nature Red List. It is recognised by the Convention on Biological Diversity as a significant component of the Global Taxonomy Initiative and a contribution to Target 1 of the Global Strategy for Plant Conservation.
The syntax used to transform the original data to a cleansed \R format, can be found here: \url{https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R}.
}
\examples{
# Get version info of included data set
catalogue_of_life_version()
# Get a note when a species was renamed
mo_shortname("Chlamydophila psittaci")
# Get any property from the entire taxonomic tree for all included species
mo_class("Escherichia coli")
mo_family("Escherichia coli")
mo_gramstain("Escherichia coli") # based on kingdom and phylum, see ?mo_gramstain
mo_ref("Escherichia coli")
# Do not get mistaken - this package is about microorganisms
mo_kingdom("C. elegans")
mo_name("C. elegans")
}
\seealso{
Data set \link{microorganisms} for the actual data. \cr
Function \code{\link[=as.mo]{as.mo()}} to use the data for intelligent determination of microorganisms.
}

28
man/catalogue_of_life_version.Rd
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@ -1,28 +0,0 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/catalogue_of_life.R
\name{catalogue_of_life_version}
\alias{catalogue_of_life_version}
\title{Version info of included Catalogue of Life}
\usage{
catalogue_of_life_version()
}
\value{
a \link{list}, which prints in pretty format
}
\description{
This function returns information about the included data from the Catalogue of Life.
}
\details{
For LPSN, see \link{microorganisms}.
}
\section{Catalogue of Life}{
\if{html}{\figure{logo_col.png}{options: height="40" style=margin-bottom:"5"} \cr}
This package contains the complete taxonomic tree of almost all microorganisms (~71,000 species) from the authoritative and comprehensive Catalogue of Life (CoL, \url{http://www.catalogueoflife.org}). The CoL is the most comprehensive and authoritative global index of species currently available. Nonetheless, we supplemented the CoL data with data from the List of Prokaryotic names with Standing in Nomenclature (LPSN, \href{https://lpsn.dsmz.de}{lpsn.dsmz.de}). This supplementation is needed until the \href{https://github.com/CatalogueOfLife/general}{CoL+ project} is finished, which we await.
\link[=catalogue_of_life]{Click here} for more information about the included taxa. Check which versions of the CoL and LPSN were included in this package with \code{\link[=catalogue_of_life_version]{catalogue_of_life_version()}}.
}
\seealso{
\link{microorganisms}
}

58
man/custom_eucast_rules.Rd
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@ -24,43 +24,87 @@ Some organisations have their own adoption of EUCAST rules. This function can be
If you are familiar with the \code{\link[dplyr:case_when]{case_when()}} function of the \code{dplyr} package, you will recognise the input method to set your own rules. Rules must be set using what \R considers to be the 'formula notation'. The rule itself is written \emph{before} the tilde (\code{~}) and the consequence of the rule is written \emph{after} the tilde:
\if{html}{\out{<div class="sourceCode {r}">}}\preformatted{x <- custom_eucast_rules(TZP == "S" ~ aminopenicillins == "S",
\if{html}{\out{<div class="sourceCode r">}}\preformatted{x <- custom_eucast_rules(TZP == "S" ~ aminopenicillins == "S",
TZP == "R" ~ aminopenicillins == "R")
}\if{html}{\out{</div>}}
These are two custom EUCAST rules: if TZP (piperacillin/tazobactam) is "S", all aminopenicillins (ampicillin and amoxicillin) must be made "S", and if TZP is "R", aminopenicillins must be made "R". These rules can also be printed to the console, so it is immediately clear how they work:
\if{html}{\out{<div class="sourceCode {r}">}}\preformatted{x
\if{html}{\out{<div class="sourceCode r">}}\preformatted{x
#> A set of custom EUCAST rules:
#>
#> 1. If TZP is "S" then set to S :
#> amoxicillin (AMX), ampicillin (AMP)
#>
#> 2. If TZP is "R" then set to R :
#> amoxicillin (AMX), ampicillin (AMP)
}\if{html}{\out{</div>}}
The rules (the part \emph{before} the tilde, in above example \code{TZP == "S"} and \code{TZP == "R"}) must be evaluable in your data set: it should be able to run as a filter in your data set without errors. This means for the above example that the column \code{TZP} must exist. We will create a sample data set and test the rules set:
\if{html}{\out{<div class="sourceCode {r}">}}\preformatted{df <- data.frame(mo = c("Escherichia coli", "Klebsiella pneumoniae"),
\if{html}{\out{<div class="sourceCode r">}}\preformatted{df <- data.frame(mo = c("Escherichia coli", "Klebsiella pneumoniae"),
TZP = as.rsi("R"),
ampi = as.rsi("S"),
cipro = as.rsi("S"))
df
#> mo TZP ampi cipro
#> 1 Escherichia coli R S S
#> 2 Klebsiella pneumoniae R S S
eucast_rules(df, rules = "custom", custom_rules = x, info = FALSE)
#> mo TZP ampi cipro
#> 1 Escherichia coli R R S
#> 2 Klebsiella pneumoniae R R S
}\if{html}{\out{</div>}}
}
\subsection{Using taxonomic properties in rules}{
There is one exception in variables used for the rules: all column names of the \link{microorganisms} data set can also be used, but do not have to exist in the data set. These column names are: \verb{r vector_and(colnames(microorganisms), sort = FALSE)}. Thus, this next example will work as well, despite the fact that the \code{df} data set does not contain a column \code{genus}:
There is one exception in variables used for the rules: all column names of the \link{microorganisms} data set can also be used, but do not have to exist in the data set. These column names are: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence" and "snomed". Thus, this next example will work as well, despite the fact that the \code{df} data set does not contain a column \code{genus}:
\if{html}{\out{<div class="sourceCode {r}">}}\preformatted{y <- custom_eucast_rules(TZP == "S" & genus == "Klebsiella" ~ aminopenicillins == "S",
\if{html}{\out{<div class="sourceCode r">}}\preformatted{y <- custom_eucast_rules(TZP == "S" & genus == "Klebsiella" ~ aminopenicillins == "S",
TZP == "R" & genus == "Klebsiella" ~ aminopenicillins == "R")
eucast_rules(df, rules = "custom", custom_rules = y, info = FALSE)
#> mo TZP ampi cipro
#> 1 Escherichia coli R S S
#> 2 Klebsiella pneumoniae R R S
}\if{html}{\out{</div>}}
}
\subsection{Usage of antibiotic group names}{
It is possible to define antibiotic groups instead of single antibiotics for the rule consequence, the part \emph{after} the tilde. In above examples, the antibiotic group \code{aminopenicillins} is used to include ampicillin and amoxicillin. The following groups are allowed (case-insensitive). Within parentheses are the agents that will be matched when running the rule.
\verb{r paste0(" * ", sapply(DEFINED_AB_GROUPS, function(x) paste0("\\"", tolower(gsub("^AB_", "", x)), "\\"\\\\cr(", vector_and(ab_name(eval(parse(text = x), envir = asNamespace("AMR")), language = NULL, tolower = TRUE), quotes = FALSE), ")"), USE.NAMES = FALSE), "\\n", collapse = "")}
\itemize{
\item "aminoglycosides"\cr(amikacin, amikacin/fosfomycin, amphotericin B-high, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin and tobramycin-high)
\item "aminopenicillins"\cr(amoxicillin and ampicillin)
\item "antifungals"\cr(5-fluorocytosine, amphotericin B, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole and voriconazole)
\item "antimycobacterials"\cr(4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, morinamide, p-aminosalicylic acid, pretomanid, prothionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, simvastatin/fenofibrate, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone/isoniazid, tiocarlide and viomycin)
\item "betalactams"\cr(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, biapenem, carbenicillin, carindacillin, cefacetrile, cefaclor, cefadroxil, cefaloridine, cefamandole, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/enzyme inhibitor, ceftolozane/tazobactam, ceftriaxone, cefuroxime, cefuroxime axetil, cephalexin, cephalothin, cephapirin, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, methicillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, phenethicillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, tebipenem, temocillin, ticarcillin and ticarcillin/clavulanic acid)
\item "carbapenems"\cr(biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil and tebipenem)
\item "cephalosporins"\cr(cefacetrile, cefaclor, cefadroxil, cefaloridine, cefamandole, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/enzyme inhibitor, ceftolozane/tazobactam, ceftriaxone, cefuroxime, cefuroxime axetil, cephalexin, cephalothin, cephapirin, cephradine, latamoxef and loracarbef)
\item "cephalosporins_1st"\cr(cefacetrile, cefadroxil, cefaloridine, cefatrizine, cefazedone, cefazolin, cefroxadine, ceftezole, cephalexin, cephalothin, cephapirin and cephradine)
\item "cephalosporins_2nd"\cr(cefaclor, cefamandole, cefmetazole, cefonicid, ceforanide, cefotetan, cefotiam, cefoxitin, cefoxitin screening, cefprozil, cefuroxime, cefuroxime axetil and loracarbef)
\item "cephalosporins_3rd"\cr(cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefetamet, cefetamet pivoxil, cefixime, cefmenoxime, cefodizime, cefoperazone, cefoperazone/sulbactam, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotiam hexetil, cefovecin, cefpimizole, cefpiramide, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefsulodin, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftriaxone and latamoxef)
\item "cephalosporins_4th"\cr(cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetecol, cefoselis, cefozopran, cefpirome and cefquinome)
\item "cephalosporins_5th"\cr(ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril, ceftolozane/enzyme inhibitor and ceftolozane/tazobactam)
\item "cephalosporins_except_caz"\cr(cefacetrile, cefaclor, cefadroxil, cefaloridine, cefamandole, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/enzyme inhibitor, ceftolozane/tazobactam, ceftriaxone, cefuroxime, cefuroxime axetil, cephalexin, cephalothin, cephapirin, cephradine, latamoxef and loracarbef)
\item "fluoroquinolones"\cr(besifloxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nifuroquine, norfloxacin, ofloxacin, orbifloxacin, pazufloxacin, pefloxacin, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin and trovafloxacin)
\item "glycopeptides"\cr(avoparcin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin and vancomycin-macromethod)
\item "glycopeptides_except_lipo"\cr(avoparcin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin and vancomycin-macromethod)
\item "lincosamides"\cr(acetylmidecamycin, acetylspiramycin, clindamycin, gamithromycin, kitasamycin, lincomycin, meleumycin, nafithromycin, pirlimycin, primycin, solithromycin, tildipirosin, tilmicosin, tulathromycin, tylosin and tylvalosin)
\item "lipoglycopeptides"\cr(dalbavancin, oritavancin and telavancin)
\item "macrolides"\cr(acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, pirlimycin, primycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin and tylvalosin)
\item "oxazolidinones"\cr(cadazolid, cycloserine, linezolid, tedizolid and thiacetazone)
\item "penicillins"\cr(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, carbenicillin, carindacillin, cefepime/nacubactam, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, methicillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, penamecillin, penicillin/novobiocin, penicillin/sulbactam, phenethicillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, temocillin, ticarcillin and ticarcillin/clavulanic acid)
\item "polymyxins"\cr(colistin, polymyxin B and polymyxin B/polysorbate 80)
\item "quinolones"\cr(besifloxacin, cinoxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nifuroquine, nitroxoline, norfloxacin, ofloxacin, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin and trovafloxacin)
\item "streptogramins"\cr(pristinamycin and quinupristin/dalfopristin)
\item "tetracyclines"\cr(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, tetracycline and tigecycline)
\item "tetracyclines_except_tgc"\cr(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline and tetracycline)
\item "trimethoprims"\cr(brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim and trimethoprim/sulfamethoxazole)
\item "ureidopenicillins"\cr(azlocillin, mezlocillin, piperacillin and piperacillin/tazobactam)
}
}
}

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